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	<title>我爱自然语言处理 &#187; 自然语言处理</title>
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		<title>机器翻译新闻一则：SDL公司收购Language Weaver</title>
		<link>http://www.52nlp.cn/%e6%9c%ba%e5%99%a8%e7%bf%bb%e8%af%91%e6%96%b0%e9%97%bb%e4%b8%80%e5%88%99-sdl%e5%85%ac%e5%8f%b8%e6%94%b6%e8%b4%adlanguage-weaver</link>
		<comments>http://www.52nlp.cn/%e6%9c%ba%e5%99%a8%e7%bf%bb%e8%af%91%e6%96%b0%e9%97%bb%e4%b8%80%e5%88%99-sdl%e5%85%ac%e5%8f%b8%e6%94%b6%e8%b4%adlanguage-weaver#comments</comments>
		<pubDate>Wed, 21 Jul 2010 15:27:37 +0000</pubDate>
		<dc:creator>52nlp</dc:creator>
				<category><![CDATA[机器翻译]]></category>
		<category><![CDATA[自然语言处理]]></category>
		<category><![CDATA[Kevin Knight]]></category>
		<category><![CDATA[Language Weaver]]></category>
		<category><![CDATA[SDL]]></category>
		<category><![CDATA[统计机器翻译]]></category>

		<guid isPermaLink="false">http://www.52nlp.cn/?p=3420</guid>
		<description><![CDATA[　　也许是时下流行收购吧，前天刚谈了“Google收购语义网公司Metaweb”，没想到今天又发现“SDL公司收购Language Weaver”。Language Weaver是我非常崇拜的统计机器翻译公司，曾经在这里写过”自然语言处理公司巡礼七：Language Weaver“，没想到也被收购了！以下是上述新闻摘录的要点：
　　英国梅登黑德——作为一家领先的全球信息管理方案提供商，SDL 2010年7月15日宣布已签署收购 Language Weaver Inc.（以下简称“Language Weaver”） 的协议。Language Weaver 是统计机器翻译领域的先驱，对其收购不仅仅是为了将最好的自动翻译技术移植到 SDL 全球信息管理平台中，事实上它的意义远大于此。将安全的机器翻译技术集成到翻译供应链的各个环节中，可使企业和政府能更快更高效地翻译超大容量的内容，以满足当今急剧增长的网络世界对海量信息的需求。经股东同意，此次交易共购买 Language Weaver 85% 以上的资本所有权，预计将于 2010 年 7 月底完成交易。
　　．．．
　　Language Weaver 总部设在美国加利福尼亚州洛杉矶市，在美国、欧洲和日本都有办公室，拥有雇员96 名。公司与南加利福尼亚大学信息科学研究院（机器翻译研究的领军机构）合作紧密。双方科学家都在共同努力，以期进一步研究和提高统计机器翻译方法。Language Weaver 的创始人Daniel Marcu 和 Kevin Knight 均为统计机器翻译领域的领军人物，他们将继续留任公司。因为 Language Weaver 技术的品质与性能已达到全新水平，Mark Tapling新近提拔成为了 Language Weaver 的 CEO， 以便进一步加强公司的商业化进程，Mark Tapling 也将继续留任公司。且当前并无对 Language Weaver 公司进行裁员的计划。
　　．．．
　　 “尽管谷歌翻译已成为消费者即时翻译的标准，但我们发现，大多数企业希望拥有自己的自动翻译技术，”Language Weaver 的董事长兼执行总裁Mark Tapling 说到，“使用Language Weaver，可保证您的内容安全、保密；它遵循翻译工作流程，而且可以很容易地集成到其他系统中。它也可以提供质量排序和受训系统，以提供值得您信赖的质量。它遵守诸如公司品牌和翻译一致性这类要求。Language Weaver的研发团队，不断推进统计机器翻译研究的极限，同时为企业和政府机构提供人际交往解决方案。SDL的收购将大大增强Language Weaver团队解决问题的能力，并向市场推出独特的高价值机器翻译产品和解决方案。”
　　．．．
　　今天，自动翻译仅占翻译市场总量的 1% [...]


相关文章:<ol><li><a href='http://www.52nlp.cn/natural-language-processing-company-language-weaver' rel='bookmark' title='Permanent Link: 自然语言处理公司巡礼七：Language Weaver'>自然语言处理公司巡礼七：Language Weaver</a></li>
<li><a href='http://www.52nlp.cn/%e6%9c%ba%e5%99%a8%e7%bf%bb%e8%af%91%e6%96%b0%e9%97%bb%e4%b8%80%e5%88%99' rel='bookmark' title='Permanent Link: 机器翻译新闻一则'>机器翻译新闻一则</a></li>
<li><a href='http://www.52nlp.cn/natural-language-processing-and-computational-linguistics-books-summary-five-machine-translation' rel='bookmark' title='Permanent Link: 自然语言处理与计算语言学书籍汇总之五：机器翻译'>自然语言处理与计算语言学书籍汇总之五：机器翻译</a></li>
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<li><a href='http://www.52nlp.cn/natural-language-processing-company-systran' rel='bookmark' title='Permanent Link: 自然语言处理公司巡礼四：Systran'>自然语言处理公司巡礼四：Systran</a></li>
<li><a href='http://www.52nlp.cn/natural-language-processing-and-machine-translation-faq' rel='bookmark' title='Permanent Link: 自然语言处理与机器翻译FAQ'>自然语言处理与机器翻译FAQ</a></li>
<li><a href='http://www.52nlp.cn/statistical-machine-translation-tutorial-reading' rel='bookmark' title='Permanent Link: 统计机器翻译文献阅读指南'>统计机器翻译文献阅读指南</a></li>
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</ol>]]></description>
			<content:encoded><![CDATA[<p>　　也许是时下流行收购吧，前天刚谈了“<a href="http://www.52nlp.cn/%E8%AF%AD%E4%B9%89%E7%BD%91%E6%96%B0%E9%97%BB%E4%B8%80%E5%88%99-google%E6%94%B6%E8%B4%AD%E8%AF%AD%E4%B9%89%E7%BD%91%E5%85%AC%E5%8F%B8metaweb">Google收购语义网公司Metaweb</a>”，没想到今天又发现“<a href="http://news.dayoo.com/china/201007/20/54502_13347123.htm">SDL公司收购Language Weaver</a>”。Language Weaver是我非常崇拜的统计机器翻译公司，曾经在这里写过”<a href="http://www.52nlp.cn/natural-language-processing-company-language-weaver">自然语言处理公司巡礼七：Language Weaver</a>“，没想到也被收购了！以下是上述新闻摘录的要点：<span id="more-3420"></span></p>
<blockquote><p>　　英国梅登黑德——作为一家领先的全球信息管理方案提供商，SDL 2010年7月15日宣布已签署收购 Language Weaver Inc.（以下简称“Language Weaver”） 的协议。Language Weaver 是统计机器翻译领域的先驱，对其收购不仅仅是为了将最好的自动翻译技术移植到 SDL 全球信息管理平台中，事实上它的意义远大于此。将安全的机器翻译技术集成到翻译供应链的各个环节中，可使企业和政府能更快更高效地翻译超大容量的内容，以满足当今急剧增长的网络世界对海量信息的需求。经股东同意，此次交易共购买 Language Weaver 85% 以上的资本所有权，预计将于 2010 年 7 月底完成交易。<br />
　　．．．<br />
　　Language Weaver 总部设在美国加利福尼亚州洛杉矶市，在美国、欧洲和日本都有办公室，拥有雇员96 名。公司与南加利福尼亚大学信息科学研究院（机器翻译研究的领军机构）合作紧密。双方科学家都在共同努力，以期进一步研究和提高统计机器翻译方法。Language Weaver 的创始人Daniel Marcu 和 Kevin Knight 均为统计机器翻译领域的领军人物，他们将继续留任公司。因为 Language Weaver 技术的品质与性能已达到全新水平，Mark Tapling新近提拔成为了 Language Weaver 的 CEO， 以便进一步加强公司的商业化进程，Mark Tapling 也将继续留任公司。且当前并无对 Language Weaver 公司进行裁员的计划。<br />
　　．．．<br />
　　 “尽管谷歌翻译已成为消费者即时翻译的标准，但我们发现，大多数企业希望拥有自己的自动翻译技术，”Language Weaver 的董事长兼执行总裁Mark Tapling 说到，“使用Language Weaver，可保证您的内容安全、保密；它遵循翻译工作流程，而且可以很容易地集成到其他系统中。它也可以提供质量排序和受训系统，以提供值得您信赖的质量。它遵守诸如公司品牌和翻译一致性这类要求。Language Weaver的研发团队，不断推进统计机器翻译研究的极限，同时为企业和政府机构提供人际交往解决方案。SDL的收购将大大增强Language Weaver团队解决问题的能力，并向市场推出独特的高价值机器翻译产品和解决方案。”<br />
　　．．．<br />
　　今天，自动翻译仅占翻译市场总量的 1% 左右（据IDC提供的数据，约为100-150亿美元）但市场分析人士预计，无论是整个翻译市场，还是自动翻译的市场份额都将持续大幅度增长。SDL发现自动翻译能降低客户 30％ 到50％ 的翻译成本，与此同时，已翻译内容的市场投放时间可缩短 50% 以上。</p></blockquote>
<p>注：转载请注明出处“<a href="http://www.52nlp.cn">我爱自然语言处理</a>”：<a href="http://www.52nlp.cn">www.52nlp.cn</a></p>
<p>本文链接地址：<a href="http://www.52nlp.cn/机器翻译新闻一则-sdl公司收购language-weaver">http://www.52nlp.cn/机器翻译新闻一则-sdl公司收购language-weaver</a></p>
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<li><a href='http://www.52nlp.cn/%e6%9c%ba%e5%99%a8%e7%bf%bb%e8%af%91%e6%96%b0%e9%97%bb%e4%b8%80%e5%88%99' rel='bookmark' title='Permanent Link: 机器翻译新闻一则'>机器翻译新闻一则</a></li>
<li><a href='http://www.52nlp.cn/natural-language-processing-and-computational-linguistics-books-summary-five-machine-translation' rel='bookmark' title='Permanent Link: 自然语言处理与计算语言学书籍汇总之五：机器翻译'>自然语言处理与计算语言学书籍汇总之五：机器翻译</a></li>
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<li><a href='http://www.52nlp.cn/natural-language-processing-company-systran' rel='bookmark' title='Permanent Link: 自然语言处理公司巡礼四：Systran'>自然语言处理公司巡礼四：Systran</a></li>
<li><a href='http://www.52nlp.cn/natural-language-processing-and-machine-translation-faq' rel='bookmark' title='Permanent Link: 自然语言处理与机器翻译FAQ'>自然语言处理与机器翻译FAQ</a></li>
<li><a href='http://www.52nlp.cn/statistical-machine-translation-tutorial-reading' rel='bookmark' title='Permanent Link: 统计机器翻译文献阅读指南'>统计机器翻译文献阅读指南</a></li>
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</ol></p>]]></content:encoded>
			<wfw:commentRss>http://www.52nlp.cn/%e6%9c%ba%e5%99%a8%e7%bf%bb%e8%af%91%e6%96%b0%e9%97%bb%e4%b8%80%e5%88%99-sdl%e5%85%ac%e5%8f%b8%e6%94%b6%e8%b4%adlanguage-weaver/feed</wfw:commentRss>
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		<title>语义网新闻一则：Google收购语义网公司Metaweb</title>
		<link>http://www.52nlp.cn/%e8%af%ad%e4%b9%89%e7%bd%91%e6%96%b0%e9%97%bb%e4%b8%80%e5%88%99-google%e6%94%b6%e8%b4%ad%e8%af%ad%e4%b9%89%e7%bd%91%e5%85%ac%e5%8f%b8metaweb</link>
		<comments>http://www.52nlp.cn/%e8%af%ad%e4%b9%89%e7%bd%91%e6%96%b0%e9%97%bb%e4%b8%80%e5%88%99-google%e6%94%b6%e8%b4%ad%e8%af%ad%e4%b9%89%e7%bd%91%e5%85%ac%e5%8f%b8metaweb#comments</comments>
		<pubDate>Mon, 19 Jul 2010 13:36:36 +0000</pubDate>
		<dc:creator>52nlp</dc:creator>
				<category><![CDATA[自然语言处理]]></category>
		<category><![CDATA[语义网]]></category>
		<category><![CDATA[Google]]></category>
		<category><![CDATA[Metaweb]]></category>
		<category><![CDATA[W3CHINA]]></category>

		<guid isPermaLink="false">http://www.52nlp.cn/?p=3415</guid>
		<description><![CDATA[　　这几天比较重磅的消息是“Google收购语义网公司Metaweb”，关于Metaweb，这里曾在《自然语言处理公司巡礼六：Metaweb》中介绍过：Metaweb是从事语义网（Semantic Web）技术开发的风险企业，目标是开发用于Web的语义数据存储的基础结构，是曾就职于原美国网景（Netscape）、英特尔以及 AlexaInternet等公司的人才聚集在一起，于2005年7月成立，总部设在美国旧金山。
Google产品管理主管杰克·门泽尔(Jack Menzel)发表博客文章称，该公司可以处理许多搜索请求，但Metaweb的信息可以使其处理更多搜索请求，“通过推出搜索答案等功能，我们才刚刚开始将我们对互联网的理解用于改进搜索体验”，但对于部分搜索仍然无能为力，“例如，‘美国西海岸地区学费低于3万美元的大学’或‘年龄超过40岁且获得过至少一次奥斯卡奖的演员’，这些问题都很难回答。我们之所以收购Metaweb，是因为我们相信，整合Metaweb的技术将使我们能提供更好的答案”。
　　关于此次收购，国内语义网方面知名的W3CHINA（中国万维网联盟）论坛上专门开贴讨论，其中W3China站长的评论尤为精彩：
去年三月，谷歌三位重量级搜索技术专家Alon Halevy、Peter Norvig和Fernando Pereira曾共同撰文《The Unreasonable Effectiveness of Data》（发表于IEEE Intelligent System）低估语义技术的功效，引来一阵拍砖。
当时就有人声称“谷歌不搞语义”，其实在一家商业公司里，存在走不同甚至相反路线的阵营也是很正常的事。
85公里在地球上并算不上多长的路，但这段路如果被铺设在白令海峡上，那么它将贯通美洲大陆和亚欧大陆。
　　有兴趣的读者可以关注原帖：Google购买语义网公司Metaweb，迈向语义网技术领域重要一步
注：转载请注明出处“我爱自然语言处理”：www.52nlp.cn
本文链接地址：http://www.52nlp.cn/语义网新闻一则-google收购语义网公司metaweb







   


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机器翻译新闻一则：SDL公司收购Language Weaver
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Beautiful Data-统计语言模型的应用三：分词5
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ACL Anthology 姊妹篇：ACL Anthology Network



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			<content:encoded><![CDATA[<p>　　这几天比较重磅的消息是“<a href="http://www.nytimes.com/external/readwriteweb/2010/07/16/16readwriteweb-google-makes-major-semantic-web-play-acquir-62129.html?emc=eta1"target=_blank>Google收购语义网公司Metaweb</a>”，关于Metaweb，这里曾在《<a href="http://www.52nlp.cn/natural-language-processing-company-metaweb">自然语言处理公司巡礼六：Metaweb</a>》中介绍过：Metaweb是从事语义网（Semantic Web）技术开发的风险企业，目标是开发用于Web的语义数据存储的基础结构，是曾就职于原美国网景（Netscape）、英特尔以及 AlexaInternet等公司的人才聚集在一起，于2005年7月成立，总部设在美国旧金山。<span id="more-3415"></span></p>
<blockquote><p>Google产品管理主管杰克·门泽尔(Jack Menzel)发表博客文章称，该公司可以处理许多搜索请求，但Metaweb的信息可以使其处理更多搜索请求，“通过推出搜索答案等功能，我们才刚刚开始将我们对互联网的理解用于改进搜索体验”，但对于部分搜索仍然无能为力，“例如，‘美国西海岸地区学费低于3万美元的大学’或‘年龄超过40岁且获得过至少一次奥斯卡奖的演员’，这些问题都很难回答。我们之所以收购Metaweb，是因为我们相信，整合Metaweb的技术将使我们能提供更好的答案”。</p></blockquote>
<p>　　关于此次收购，国内语义网方面知名的W3CHINA（中国万维网联盟）论坛上专门开贴讨论，其中W3China站长的评论尤为精彩：</p>
<blockquote><p>去年三月，谷歌三位重量级搜索技术专家Alon Halevy、Peter Norvig和Fernando Pereira曾共同撰文《The Unreasonable Effectiveness of Data》（发表于IEEE Intelligent System）低估语义技术的功效，引来一阵拍砖。</p>
<p>当时就有人声称“谷歌不搞语义”，其实在一家商业公司里，存在走不同甚至相反路线的阵营也是很正常的事。</p>
<p>85公里在地球上并算不上多长的路，但这段路如果被铺设在白令海峡上，那么它将贯通美洲大陆和亚欧大陆。</p></blockquote>
<p>　　有兴趣的读者可以关注原帖：<a href="http://bbs.w3china.org/dispbbs.asp?boardID=2&#038;ID=85780"target=_blank>Google购买语义网公司Metaweb，迈向语义网技术领域重要一步</a></p>
<p>注：转载请注明出处“<a href="http://www.52nlp.cn">我爱自然语言处理</a>”：<a href="http://www.52nlp.cn">www.52nlp.cn</a></p>
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		<title>ACL 2010 Best Paper Awards</title>
		<link>http://www.52nlp.cn/acl-2010-best-paper-awards</link>
		<comments>http://www.52nlp.cn/acl-2010-best-paper-awards#comments</comments>
		<pubDate>Thu, 15 Jul 2010 14:21:19 +0000</pubDate>
		<dc:creator>52nlp</dc:creator>
				<category><![CDATA[自然语言处理]]></category>
		<category><![CDATA[计算语言学]]></category>
		<category><![CDATA[ACL]]></category>
		<category><![CDATA[ACL 2010]]></category>
		<category><![CDATA[Best long paper]]></category>
		<category><![CDATA[Best Paper Awards]]></category>
		<category><![CDATA[Best short paper]]></category>
		<category><![CDATA[IBM Best student paper]]></category>
		<category><![CDATA[Lifetime Achievement Award]]></category>

		<guid isPermaLink="false">http://www.52nlp.cn/?p=3404</guid>
		<description><![CDATA[　　ACL 2010官方主页似乎在前几天已经确定好了本次大会的Best Paper Awards，在其Awards页面里，不仅给出了本次大会的Best long paper, Best short paper, IBM Best student paper，而且包括其在会议期间Presented time. 
Best long paper
Beyond NomBank: A Study of Implicit Arguments for Nominal Predicates
Matthew Gerber and Joyce Chai
Despite its substantial coverage, NomBank does not account for all within-sentence arguments and ignores extrasentential arguments altogether. These arguments, which we call implicit, are important to [...]


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			<content:encoded><![CDATA[<p>　　ACL 2010官方主页似乎在前几天已经确定好了本次大会的Best Paper Awards，在其<a href="http://acl2010.org/awards.html"target=_blank>Awards</a>页面里，不仅给出了本次大会的Best long paper, Best short paper, IBM Best student paper，而且包括其在会议期间Presented time. <span id="more-3404"></span></p>
<p><strong>Best long paper</strong><br />
Beyond NomBank: A Study of Implicit Arguments for Nominal Predicates<br />
Matthew Gerber and Joyce Chai</p>
<blockquote><p>Despite its substantial coverage, NomBank does not account for all within-sentence arguments and ignores extrasentential arguments altogether. These arguments, which we call implicit, are important to semantic processing, and their recovery could potentially benefit many NLP applications. We present a study of implicit arguments for a select group of frequent nominal predicates. We show that implicit arguments are pervasive for these predicates, adding 65% to the coverage of NomBank. We demonstrate the feasibility of recovering implicit arguments with a supervised classification model. Our results and analyses provide a baseline for future work on this emerging task.</p></blockquote>
<p><strong>Best short paper</strong><br />
SVD and Clustering for Unsupervised POS Tagging<br />
Michael Lamar, Yariv Maron, Mark Johnson, Elie Bienenstock</p>
<blockquote><p>
We revisit the algorithm of Schütze (1995) for unsupervised part-of-speech tagging. The algorithm uses reduced-rank singular value decomposition followed by clustering to extract latent features from context distributions. As implemented here, it achieves state-of-the-art tagging accuracy at considerably less cost than more recent methods. It can also produce a range of finer-grained taggings, with potential applications to various tasks.</p></blockquote>
<p><strong>IBM Best student paper</strong><br />
Extracting Social Networks from Literary Fiction<br />
David Elson,  Nicholas Dames,  Kathleen McKeown<br />
（注：该文也是一篇long paper，作者是学生）</p>
<blockquote><p>
We present a method for extracting social networks from literature, namely, nineteenth-century British novels and serials. We derive the networks from dialogue interactions, and thus our method depends on the ability to determine when two characters are in conversation. Our approach involves character name chunking, quoted speech attribution and conversation detection given the set of quotes. We extract features from the social networks and examine their correlation with one another, as well as with metadata such as the novel’s setting. Our results provide evidence that the majority of novels in this time period do not fit two characterizations provided by literacy scholars. Instead, our results suggest an alternative explanation for differences in social networks.</p></blockquote>
<p>　　Best Paper Awards是由ACL的一个专门委员会评选出的，将在大会结束时进行颁奖。ACL 2010还有一个”Lifetime Achievement Award（终生成就奖）“，不过目前还没有揭晓获奖者。关于这个奖项，ACL 2010给了一段很有意思的介绍：</p>
<blockquote><p>The ACL Lifetime Achievement Award (LTA) was instituted on the occasion of the Association&#8217;s 40th anniversary meeting.  The award is presented for scientific achievement, of both theoretical and applied nature, in the field of Computational Linguistics.  Currently, an ACL committee nominates and selects at most one award recipient annually, considering the originality, depth, breadth, and impact of the entire body of the nominee&#8217;s work in the field. The award is a crystal trophy and the recipient is invited to give a 45-minute speech on his or her view of the development of Computational Linguistics at the annual meeting of the association.  As of 2004, the speech has been subsequently published in the Association&#8217;s journal, Computational Linguistics.  The speech is introduced by the announcement of the award winner, whose identity is not made public until that time.</p></blockquote>
<p>　　Lifetime Achievement Award（终生成就奖）每届最多只授予一位对于自然语言处理与计算语言学有着举足轻重影响的候选者，此前获得该奖项的分别是：Aravind Joshi (2002), Makoto Nagao (2003), Karen Spärck Jones (2004), Martin Kay (2005), Eva Hajicová (2006), Lauri Karttunen (2007), Yorick Wilks (2008) and Fred Jelinek (2009).  </p>
<p>注：转载请注明出处“<a href="http://www.52nlp.cn">我爱自然语言处理</a>”：<a href="http://www.52nlp.cn">www.52nlp.cn</a></p>
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		<title>ACL 2010文章已可下载</title>
		<link>http://www.52nlp.cn/acl-2010%e6%96%87%e7%ab%a0%e5%b7%b2%e5%8f%af%e4%b8%8b%e8%bd%bd</link>
		<comments>http://www.52nlp.cn/acl-2010%e6%96%87%e7%ab%a0%e5%b7%b2%e5%8f%af%e4%b8%8b%e8%bd%bd#comments</comments>
		<pubDate>Sun, 11 Jul 2010 14:30:49 +0000</pubDate>
		<dc:creator>52nlp</dc:creator>
				<category><![CDATA[自然语言处理]]></category>
		<category><![CDATA[计算语言学]]></category>
		<category><![CDATA[ACL]]></category>
		<category><![CDATA[ACL 2010]]></category>
		<category><![CDATA[ACL Anthology]]></category>
		<category><![CDATA[Min-Yen Kan]]></category>

		<guid isPermaLink="false">http://www.52nlp.cn/?p=3398</guid>
		<description><![CDATA[　　晚上收到ACL Anthology负责人Min-Yen Kan发给ACL Anthology Google Group的邮件，通知说目前ACL 2010的文章已经可以下载，包括full papers, short papers, student research workshop papers, demonstrations, tutorial abstracts以及所有的workshops的Paper，才想起今天（7月11号）ACL 2010会议召开。以下是具体的下载地址，有兴趣的读者可以关注一下。
　　一、ACL 2010大会论文集：
　　Proceedings of the ACL 2010 conference can be found here:
　　http://www.aclweb.org/anthology/P/P10/
　　These include both volumes: (I) full papers and (II) short papers,student research workshop papers, demonstrations and tutorial abstracts.
　　二、Workshop论文集：
　　The proceedings of the workshops and conferences co-located with ACL 2010 [...]


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<li><a href='http://www.52nlp.cn/coling-2010-prospect' rel='bookmark' title='Permanent Link: COLING 2010前瞻——规则与统计共舞，语言随计算齐飞'>COLING 2010前瞻——规则与统计共舞，语言随计算齐飞</a></li>
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</ol>]]></description>
			<content:encoded><![CDATA[<p>　　晚上收到<a href="http://www.52nlp.cn/acl-anthology-computational-linguistics-digital-archive">ACL Anthology</a>负责人Min-Yen Kan发给ACL Anthology Google Group的邮件，通知说目前ACL 2010的文章已经可以下载，包括full papers, short papers, student research workshop papers, demonstrations, tutorial abstracts以及所有的workshops的Paper，才想起今天（7月11号）ACL 2010会议召开。以下是具体的下载地址，有兴趣的读者可以关注一下。<span id="more-3398"></span></p>
<p>　　一、ACL 2010大会论文集：<br />
　　Proceedings of the ACL 2010 conference can be found here:<br />
　　<a href=" http://www.aclweb.org/anthology/P/P10/"target=_blank>http://www.aclweb.org/anthology/P/P10/</a><br />
　　These include both volumes: (I) full papers and (II) short papers,student research workshop papers, demonstrations and tutorial abstracts.</p>
<p>　　二、Workshop论文集：<br />
　　The proceedings of the workshops and conferences co-located with ACL 2010 are now online.<br />
　　<a href="http://www.aclweb.org/anthology/W/W10/"target=_blank>http://www.aclweb.org/anthology/W/W10/</a><br />
(scroll towards the bottom of the table of contents)</p>
<p>* Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR<br />
* Fourth Linguistic Annotation Workshop<br />
* 2010 Workshop on Biomedical Natural Language Processing<br />
* 2010 Workshop on Cognitive Modeling and Computational Linguistics<br />
* 2010 Workshop on NLP and Linguistics: Finding the Common Ground<br />
* 11th Meeting of the ACL Special Interest Group on Computational Morphology and Phonology<br />
* TextGraphs-5 &#8211; 2010 Workshop on Graph-based Methods for Natural Language Processing<br />
* 2010 Named Entities Workshop<br />
* 2010 Workshop on Applications of Tree Automata in Natural Language Processing<br />
* 2010 Workshop on Domain Adaptation for Natural Language Processing<br />
* 2010 Workshop on Companionable Dialogue Systems<br />
* 2010 Workshop on GEometrical Models of Natural Language Semantics</p>
<p>注：转载请注明出处“<a href="http://www.52nlp.cn">我爱自然语言处理</a>”：<a href="http://www.52nlp.cn">www.52nlp.cn</a></p>
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		</item>
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		<title>自然语言处理与世界杯</title>
		<link>http://www.52nlp.cn/%e8%87%aa%e7%84%b6%e8%af%ad%e8%a8%80%e5%a4%84%e7%90%86%e4%b8%8e%e4%b8%96%e7%95%8c%e6%9d%af</link>
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		<pubDate>Wed, 30 Jun 2010 13:00:36 +0000</pubDate>
		<dc:creator>52nlp</dc:creator>
				<category><![CDATA[自然语言处理]]></category>
		<category><![CDATA[随笔]]></category>
		<category><![CDATA[世界杯]]></category>
		<category><![CDATA[情感分析]]></category>
		<category><![CDATA[章成志]]></category>
		<category><![CDATA[聚类]]></category>

		<guid isPermaLink="false">http://www.52nlp.cn/?p=3389</guid>
		<description><![CDATA[　　自然语言处理与世界杯似乎没啥关系，不过今晚世界杯没有比赛了，我也可以回来照顾一下52nlp了。但是这两者的确没什么关系，我简单的Google了一下“自然语言处理 &#038; 世界杯”，没有什么好的材料，就先从读者评论说起吧。
　　读者Brishen评论：“可不可以对网络上赛前的言论做sentiment analysis来预测一下比赛结果呢？” 估计Brishen对Sentiment analysis（情感分析）有比较深的认识，我个人没有任何这方面的经验，不过感觉是一个不错的方向，不仅仅对于世界杯。如果读者有这方面的经验，欢迎在这里讨论。
　　聚类在自然语言处理中也有比较重要的应用，譬如词的聚类或者文本聚类。章成志老师近期写了一篇《世界杯比赛规则与数据聚类》，大概是与世界杯相关的最具科普性的一篇博文了，和自然语言处理也能扯点关系，以下全文转载自章成志老师的博客。
　　　　　　　　　世界杯比赛规则与数据聚类
       　　应该有很多博友像我一样，这段时间可能要花些时间看世界杯。有些博友还会发些心得。俺就从数据聚类的角度，来对世界杯比赛规则进行“重认识”一下，呵呵。
       　　先交代下基础背景知识，内行直接跳过本段，呵呵。数据聚类包括划分聚类、层次聚类等、基于模型的聚类等基本模式。划分聚类中最经典的方法就是K-均值聚类，需要事先给定初始点和聚类类目数。层次聚类中最常用的是HAC聚类，事先两两求出相似度，将最相似的或者最不相似的连接起来呢，然后再求次相似的，一直到所有点的都被连接为止。近年来，基于模型的聚类越来越火，可以将基于竞争的聚类方法划入这个类别。07年Frey提出的AP聚类方法更是被大量引用。
      　　再结合数据聚类，说下世界杯比赛规则。
      　　1. 首先，小组划分，是做基于约束的划分聚类：    
      　　(1) 经过预选赛入围的32只球队，被划分为4个档次，其中第一档中的8支球队作为种子队 （32个数据，8个聚类类目，将以往世界排名作为权重，选择初始聚类中心，当然东道主特殊，直接作为种子）；
      　　(2) 剩余球队按照其档次和所在洲的约束，进行抽签划分到相应的小组中（24个数据按照一定的规则约束后，随机分配到每个聚类中心的所在组中）；
 　　   [...]


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			<content:encoded><![CDATA[<p>　　自然语言处理与世界杯似乎没啥关系，不过今晚世界杯没有比赛了，我也可以回来照顾一下52nlp了。但是这两者的确没什么关系，我简单的Google了一下“自然语言处理 &#038; 世界杯”，没有什么好的材料，就先从读者评论说起吧。<span id="more-3389"></span><br />
　　读者Brishen评论：“可不可以对网络上赛前的言论做sentiment analysis来预测一下比赛结果呢？” 估计Brishen对Sentiment analysis（情感分析）有比较深的认识，我个人没有任何这方面的经验，不过感觉是一个不错的方向，不仅仅对于世界杯。如果读者有这方面的经验，欢迎在这里讨论。<br />
　　聚类在自然语言处理中也有比较重要的应用，譬如词的聚类或者文本聚类。章成志老师近期写了一篇《<a href="http://www.sciencenet.cn/m/user_content.aspx?id=339277"target=_blank>世界杯比赛规则与数据聚类</a>》，大概是与世界杯相关的最具科普性的一篇博文了，和自然语言处理也能扯点关系，以下全文转载自<a href="http://www.sciencenet.cn/u/timy/">章成志老师的博客</a>。</p>
<blockquote><p>　　　　　　　　　世界杯比赛规则与数据聚类</p>
<p>       　　应该有很多博友像我一样，这段时间可能要花些时间看世界杯。有些博友还会发些心得。俺就从数据聚类的角度，来对世界杯比赛规则进行“重认识”一下，呵呵。</p>
<p>       　　先交代下基础背景知识，内行直接跳过本段，呵呵。数据聚类包括划分聚类、层次聚类等、基于模型的聚类等基本模式。划分聚类中最经典的方法就是K-均值聚类，需要事先给定初始点和聚类类目数。层次聚类中最常用的是HAC聚类，事先两两求出相似度，将最相似的或者最不相似的连接起来呢，然后再求次相似的，一直到所有点的都被连接为止。近年来，基于模型的聚类越来越火，可以将基于竞争的聚类方法划入这个类别。07年Frey提出的AP聚类方法更是被大量引用。</p>
<p>      　　再结合数据聚类，说下世界杯比赛规则。</p>
<p>      　　1. 首先，小组划分，是做基于约束的划分聚类：    </p>
<p>      　　(1) 经过预选赛入围的32只球队，被划分为4个档次，其中第一档中的8支球队作为种子队 （32个数据，8个聚类类目，将以往世界排名作为权重，选择初始聚类中心，当然东道主特殊，直接作为种子）；</p>
<p>      　　(2) 剩余球队按照其档次和所在洲的约束，进行抽签划分到相应的小组中（24个数据按照一定的规则约束后，随机分配到每个聚类中心的所在组中）；</p>
<p> 　　     2. 然后，正式比赛，是做层次聚类：</p>
<p>      　　(1) 小组确定后，每组四个对，两两求“相似度”，就是说两两打一场，胜的权重给3，平了给1，输了给0，每小组的6场赛事结束后，得到每个队的总体权重（当然了，有可能还要考虑净胜球，相互战绩啥的），那么小组中排名前2的队作为连接点参与下一个层次的聚类。（这里，两两求相似度，完全是基于竞争的，整个比赛阶段基于竞争的层次聚类）；</p>
<p>      　　(2) 淘汰赛阶段，直接竞争，做二分聚类，胜的参加下一轮聚类；</p>
<p>　　(3) 直到最后两支最牛的打决赛，冠军队成为了根节点。</p>
<p>     　　 3. 聚类结束，参数重新分配，准备4年后的聚类，呵呵。</p>
<p>　　     所以，世界杯做了大量的约束，注意比赛的观赏性，用了比较简单公平的方法，在较短时间内确定聚类层次关系。</p>
<p>     　　如果是动物界打比赛，可能又是另一个场景，完全自由随机的打，最强的完全有可能因为体力不支，提早被淘汰而成不了冠军。</p>
<p>　　    以上仅供娱乐参考，推理和比喻不当地方，请博友指出，谢谢。</p></blockquote>
<p>　　关于自然语言处理与世界杯，不知道读者朋友还能想到些什么？</p>
<p>注：转载请注明出处“<a href="http://www.52nlp.cn">我爱自然语言处理</a>”：<a href="http://www.52nlp.cn">www.52nlp.cn</a></p>
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		<title>COLING 2010: List of Accepted Papers (Oral)</title>
		<link>http://www.52nlp.cn/coling-2010-list-of-accepted-papers-oral</link>
		<comments>http://www.52nlp.cn/coling-2010-list-of-accepted-papers-oral#comments</comments>
		<pubDate>Fri, 04 Jun 2010 12:31:58 +0000</pubDate>
		<dc:creator>52nlp</dc:creator>
				<category><![CDATA[自然语言处理]]></category>
		<category><![CDATA[计算语言学]]></category>
		<category><![CDATA[COLING]]></category>
		<category><![CDATA[COLING 2010]]></category>
		<category><![CDATA[中文信息学会]]></category>

		<guid isPermaLink="false">http://www.52nlp.cn/?p=3385</guid>
		<description><![CDATA[　　这是Coling 2010的List of Accepted Papers(Oral)，先是从水木自然语言处理社区看到，才在Coling的官方主页上找到。关于Coling本次的录用情况，水木自然语言处理版已经有过一波大讨论了，有兴趣的读者可以关注一下。 Coling是ACL之外另一个自然语言处理与计算语言学界的顶级会议，全称国际计算语言学大会(International Conference on Computational Linguistics)，每两年举办一次，第23届COLING会议将于2010年8月23日~27日在中国北京举行，由中文信息学会承办。
　　以下仅列出Oral的录用情况，关于Poster的录用情况，可以在Coling 2010官方网站的全部录用结果中找到：
　　http://www.coling-2010.org/accepted%20papers.htm
Oral
   1.  Adrian Bickerstaffe and Ingrid Zukerman. A Hierarchical Classifier Applied to Multi-way Sentiment Detection
   2. Yiping Zhou, Lan Nie and Scott Gaffney. Surface Form Resolution Based on Wikipedia
   3. Zhongwu Zhai, Bing Liu, Hua Xu and Peifa [...]


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			<content:encoded><![CDATA[<p>　　这是Coling 2010的List of Accepted Papers(Oral)，先是从水木自然语言处理社区看到，才在Coling的官方主页上找到。关于Coling本次的录用情况，水木自然语言处理版已经有过一波大讨论了，有兴趣的读者可以关注一下。 Coling是ACL之外另一个自然语言处理与计算语言学界的顶级会议，全称国际计算语言学大会(International Conference on Computational Linguistics)，每两年举办一次，第23届COLING会议将于2010年8月23日~27日在中国北京举行，由中文信息学会承办。<span id="more-3385"></span><br />
　　以下仅列出Oral的录用情况，关于Poster的录用情况，可以在Coling 2010官方网站的全部录用结果中找到：<br />
　　<a href="http://www.coling-2010.org/accepted%20papers.htm"target=_blank>http://www.coling-2010.org/accepted%20papers.htm</a></p>
<p>Oral<br />
   1.  Adrian Bickerstaffe and Ingrid Zukerman. A Hierarchical Classifier Applied to Multi-way Sentiment Detection<br />
   2. Yiping Zhou, Lan Nie and Scott Gaffney. Surface Form Resolution Based on Wikipedia<br />
   3. Zhongwu Zhai, Bing Liu, Hua Xu and Peifa Jia. Grouping Product Features Using Semi-Supervised Learning with Soft-Constraints<br />
   4. Qin Gao, Francisco Guzman and Stephan Vogel. EMDC: A Semi-supervised Approach for Word Alignment<br />
   5. George Tsatsaronis, Iraklis Varlamis and Kjetil Nørvåg. SemanticRank: Ranking Keywords and Sentences Using Semantic Graphs<br />
   6. Jian Huang, Pucktada Treeratpituk, Sarah Taylor and C. Lee Giles. Enhancing Cross Document Coreference of Web Documents with Context Similarity and Very Large Scale Text Categorization<br />
   7. Verena Henrich and Erhard Hinrichs. Standardizing Wordnets in the ISO Standard Wordnet-LMF: The Case of GermaNet<br />
   8. Ekaterina Shutova, Lin Sun and Anna Korhonen. Metaphor Identification Using Verb and Noun Clustering<br />
   9. Xiaoyan Cai, Wenjie Li and You Ouyang. Simultaneous Ranking and Clustering of Sentences: An Reinforcement Approach to Multi-Document Summarization<br />
  10. Peter Nilsson and Pierre Nugues. Automatic Discovery of Feature Sets for Dependency Parsing<br />
  11. Kavita Ganesan and ChengXiang Zhai. Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions<br />
  12. Doo Soon Kim, Ken Barker and Bruce Porter. Improving the Quality of Text Understanding by Delaying Ambiguity Resolution<br />
  13. Gumwon Hong, Chi-Ho Li, Ming Zhou and Hae-Chang Rim. An Empirical Study on Web Mining of Parallel Data<br />
  14. Nigel Collier, Reiko Matsuda Goodwin, John McCrae, Son Doan, Ai Kawazoe, Mike Conway, Asanee Kawtrakul, Koichi Takeuchi and Dinh Dien. An ontology-driven system for detecting global health events<br />
  15. Changqin Quan and Fuji Ren. An Exploration of Features for Recognizing Word Emotion<br />
  16. Jun Sun, Min Zhang and Chew Lim Tan. Discriminative Induction of Sub-Tree Alignment using Limited Labeled Data<br />
  17. Wanxiang Che and Ting Liu. Jointly Modeling WSD and SRL with Markov Logic<br />
  18. Su Nam Kim, Timothy Baldwin and Min-Yen Kan. Evaluating N-gram based Evaluation Metrics for Automatic Keyphrase Extraction<br />
  19. Yang Liu and Qun Liu. Joint Parsing and Translation<br />
  20. Shiqi Zhao, Haifeng Wang and Ting Liu. Paraphrasing with Search Engine Query Logs<br />
  21. Daniel Tse and James R. Curran. Chinese CCGbank: extracting CCG derivations from the Penn Chinese Treebank<br />
  22. Liang-Chih Yu, Hsiu-Min Shih, Yu-Ling Lai, Jui-Feng Yeh and Chung-Hsien Wu. Discriminative Training for Near-Synonym Substitution<br />
  23. Shiqi Zhao, Haifeng Wang, Xiang Lan and Ting Liu. Leveraging Multiple MT Engines for Paraphrase Generation<br />
  24. Audrey Laroche and Philippe Langlais. Revisiting Context-based Projection Methods for Term-Translation Spotting in Comparable Corpora<br />
  25. Bernd Bohnet. Top Accuracy and Fast Dependency Parsing is not a Contradiction<br />
  26. Sebastian Spiegler and Andrew van der Spuy. Ukwabelana &#8211; An open-source morphological Zulu corpus<br />
  27. Fang KONG, Guodong ZHOU, Longhua QIAN and Qiaoming ZHU. Dependency-driven Anaphoricity Determination for Coreference Resolution<br />
  28. Veronika Vincze and János Csirik. Hungarian Corpus of Light Verb Constructions<br />
  29. ding liu and daniel gildea. Semantic Role Features for Machine Translation<br />
  30. Fan Bu, Xiaoyan Zhu and Ming Li. Measuring the Non-compositionality of Multiword Expressions<br />
  31. Kai Wang and Tat-Seng Chua. Exploiting Salient Patterns for Question Detection and Question Retrieval in Community-based Question Answering<br />
  32. Matthias Hartung and Anette Frank. A Structured Vector Space Model for Hidden Attribute Meaning in Adjective-Noun Phrases<br />
  33. Guintaré Grigonyté, João Paulo Cordeiro, Rumen Moraliyski, Gaël Dias and Pavel Brazdil. Paraphrase Alignment for Synonym Evidence Discovery<br />
  34. Yaakov HaCohen-Kerner, Aharon Tayeb and Natan Ben-Dror. Plagiarism Detection in Computer Science Papers<br />
  35. Shane Bergsma and Colin Cherry. Fast and Accurate Arc Filtering for Dependency Parsing<br />
  36. Fei Huang and Bing Xiang. Feature-Rich Discriminative Phrase Rescoring for SMT<br />
  37. Katja Filippova. Multi-Sentence Compression: Finding Shortest Paths in Word Graphs<br />
  38. Roland Kuhn, Boxing Chen, George Foster and Evan Stratford. Phrase Clustering for Smoothing TM Probabilities – or, How to Extract Paraphrases from Phrase Tables<br />
  39. Josep Maria Crego, Aurélien Max and Fran?ois Yvon. Local lexical adaptation in Machine Translation through triangulation: SMT helping SMT<br />
  40. Young-Suk Lee, Bing Zhao and Xiaoqian Luo. Constituent Reordering and Syntax Models for English-to-Japanese Statistical Machine Translation<br />
  41. Mu Li, Ying-Gong Zhao, Dongdong Zhang and Ming Zhou. Adaptive Log-linear Model Parameter Selection for Statistical Machine Translation<br />
  42. Mengqiu Wang and Christopher Manning. Probabilistic Tree-Edit Models with Structured Latent Variables for Textual Entailment and Question Answering<br />
  43. Sara Salem and Samir AbdelRahman. A Multiple-Domain Ontology Builder<br />
  44. Ge Xu, Xinfan Meng and Houfeng Wang. Build Chinese Emotion Lexicons Using A Graph-based Algorithm and Multiple Resources<br />
  45. Yunfang Wu and Miaomiao Wen. Disambiguating Dynamic Sentiment Ambiguous Adjectives<br />
  46. Shasha Liao and Ralph Grishman. Filtered Ranking for Bootstrapping in Event Extraction<br />
  47. Shoushan Li, Sophia Y. M. Lee, Ying Chen, Chu-Ren Huang and Guodong Zhou. Sentiment Classification and Polarity Shifting<br />
  48. Bernard Brosseau-Villeneuve, Jian-Yun Nie and Noriko Kando. Towards an optimal weighting of context words based on distance<br />
  49. Makoto Miwa, Rune Sætre, Yusuke Miyao and Jun&#8217;ichi Tsujii. Entity-Focused Sentence Simplification for Relation Extraction<br />
  50. Chien Chin Chen and Chen-Yuan Wu. Bipolar Person Name Identification of Topic Documents Using Principal Component Analysis<br />
  51. Fangtao Li, Chao Han, Minlie Huang, Xiaoyan Zhu, Ying-Ju Xia, Shu Zhang and Hao Yu. Structure-Aware Review Mining and Summarization<br />
  52. Hassan Al-Haj and Shuly Wintner. Identifying Multi-word Expressions by Leveraging Morphological and Syntactic Idiosyncrasy<br />
  53. Nan Duan, Mu Li, Dongdong Zhang and Ming Zhou. Mixture Model-based Minimum Bayes Risk Decoding using Multiple Machine Translation Systems<br />
  54. Xinyan Xiao, Yang Liu, YoungSook Hwang, Qun Liu and Shouxun Lin. Joint Tokenization and Translation<br />
  55. Nan Duan. Translation Model Generalization using Probability Averaging for Machine Translation<br />
  56. Anders S?gaard and Christian Rish?j. Robust semi-supervised dependency parsing of German and Swedish using generalized tri-training<br />
  57. Laura Kallmeyer and Wolfgang Maier. Data-Driven Parsing with Probabilistic Linear Context-Free Rewriting Systems<br />
  58. Xian Qian, Qi Zhang, Xuangjing Huang and Lide Wu. 2D Trie for fast parsing<br />
  59. Stergos Afantenos and Nicholas Asher. Testing SDRT&#8217;s Right Frontier: Implications for Machine Learning Discourse Graphs<br />
  60. Naoaki Okazaki and Jun&#8217;ichi Tsujii. Simple and Efficient Algorithm for Approximate Dictionary Matching<br />
  61. Zarrieß Sina, Cahill Aoife, Kuhn Jonas and Rohrer Christian. Cross-Lingual Induction for Deep Broad-Coverage Syntax: A Case Study on German Participles<br />
  62. Jinhua Du and Andy Way. A Discriminative Latent Variable-Based “DE” Classifier for Chinese–English SMT<br />
  63. Makoto Miwa, Sampo Pyysalo, Tadayoshi Hara and Jun&#8217;ichi Tsujii. Evaluating Dependency Representations for Event Extraction<br />
  64. Zhemin Zhu, Delphine Bernhard and Iryna Gurevych. A Monolingual Syntactic Translation Model for Sentence Simplification<br />
  65. Ksenia Shalonova and Bruno Golenia. Weakly Supervised Morphology Learning for Agglutinating Languages Using Small Training Sets.<br />
  66. Xiaojun Wan. Towards a Unified Approach to Simultaneous Single-Document and Multi-Document Summarizations<br />
  67. Seokhwan Kim, Minwoo Jeong, Jonghoon Lee and Gary Geunbae Lee. A Cross-lingual Annotation Projection Approach for Relation Detection<br />
  68. Erwin Marsi and Emiel Krahmer. Automatic analysis of semantic similarity in comparable text through syntactic tree matching<br />
  69. Chao Shen and Tao Li. Multi-Document Summarization via the Minimum Dominating Set<br />
  70. Kun Wang, Chengqing Zong and Keh-Yih Su. A Character-Based Joint Model for Chinese Word Segmentation<br />
  71. Sung-Pil Choi and Sung-Hyon Myaeng. Simplicity is Better: Revisiting Single Kernel PPI Extraction<br />
  72. Tao Zhuang and Chengqing Zong. A Minimum Error Weighting Combination Strategy for Chinese Semantic Role Labeling<br />
  73. Hui Yang, Anne De Roeck, Alistair Willis and Bashar Nuseibeh. A Methodology for Automatic Identification of Nocuous Ambiguity<br />
  74. Toyomi Meguro, Ryuichiro Higashinaka, Yasuhiro Minami and Kohji Dohsaka. Controlling Listening-oriented Dialogue using Partially Observable Markov Decision Processes<br />
  75. Makbule Ozsoy, Ilyas Cicekli and Ferda Alpaslan. Text Summarization of Turkish Texts using Latent Semantic Analysis<br />
  76. Alena Neviarouskaya, Helmut Prendinger and Mitsuru Ishizuka. Recognition of Affect, Judgment, and Appreciation in Text<br />
  77. Mark Dredze, Paul McNamee, Delip Rao, Adam Gerber and Tim Finin. Entity Disambiguation for Knowledge Base Population<br />
  78. Minh-Thang Luong and Min-Yen Kan. Enhancing Morphological Alignment for Translating Highly Inflected Languages<br />
  79. Junhui Li, Guodong Zhou and Qiaoming Zhu. Learning the Scope of Negation via Shallow Semantic Parsing<br />
  80. Graeme Blackwood, Adrià de Gispert and William Byrne. Fluency Constraints for Minimum Bayes-Risk Decoding of Statistical Machine Translation Lattices<br />
  81. Muhua Zhu, Jingbo Zhu and Tong Xiao. Heterogeneous Parsing via Collaborative Decoding<br />
  82. Yue Lu, Huizhong Duan, Hongning Wang and ChengXiang Zhai. Exploiting Structured Ontology to Organize Scattered Online Opinions<br />
  83. Richard Johansson and Alessandro Moschitti. Reranking Models in Fine-grained Opinion Analysis<br />
  84. Ying Chen, Sophia Yat Mei Lee, Shoushan Li and Chu-Ren Huang. Emotion Cause Detection with Linguistic Constructions<br />
  85. Karthik Visweswariah, Jiri Navratil, Jeffrey Sorensen, Vijil Chenthamarakshan and Nandakishore Kambhatla. Syntax Based Reordering with Automatically Derived Rules for Improved Statistical Machine Translation<br />
  86. Bo Li and Eric Gaussier. Improving Corpus Comparability for Bilingual Lexicon Extraction from Comparable Corpora<br />
  87. Zhaopeng Tu, Yang Liu, Young-Sook Hwang, Qun Liu and Shouxun Lin. Dependency Forest for Statistical Machine Translation<br />
  88. Tomek Strzalkowski, George Aaron Broadwell, Jennifer Stromer-Galley, Samira Shaikh, Sarah Taylor and Nick Webb. Modeling Socio-Cultural Phenomena in Discourse<br />
  89. Henning Wachsmuth, Peter Prettenhofer and Benno Stein. Ef?cient Statement Identi?cation for Automatic Market Forecasting<br />
  90. Spence Green and Christopher D. Manning. Better Arabic Parsing: Baselines, Evaluations, and Analysis<br />
  91. Jian Zhang and Pascale Fung. A Rhetorical Syntax-Driven Model for Speech Summarization<br />
  92. Nuria Bel, Maria Coll and Gabriela Resnik. Automatic Detection of Non-deverbal Event Nouns for Quick Lexicon Production<br />
  93. Joakim Nivre, Laura Rimell, Ryan McDonald and Carlos Gómez Rodríguez. Evaluation of Dependency Parsers on Unbounded Dependencies<br />
  94. Lin Sun, Thierry Poibeau, Anna Korhonen and Cedric Messiant. Investigating the cross-linguistic potential of VerbNet -style classification<br />
  95. Sebastian Spiegler and Christian Monson. EMMA: A novel Evaluation Metric for Morphological Analysis<br />
  96. Naoki Yoshinaga and Masaru Kitsuregawa. Kernel Slicing: Scalable Online Training with Conjunctive Features<br />
  97. Xiaohua LIU, Kuan LI, Bo HAN, Ming ZHOU and Long JIANG. Semantic Role Labeling for News Tweets<br />
  98. Hector Llorens, Estela Saquete and Borja Navarro. TimeML Events Recognition and Classification: Learning CRF Models with Semantic Roles<br />
  99. Dmitry Davidov and Ari Rappoport. Automated Translation of Semantic Relationships<br />
 100. Bernd Bohnet, Leo Wanner, Simon Mill and Alicia Burga. Broad Coverage Multilingual Deep Sentence Generation with a Stochastic Multi-Level Realizer<br />
 101. Smruthi Mukund, Debanjan Ghosh and Rohini Srihari. Using Cross-Lingual Projections to Generate Semantic Role Labeled Annotated Corpus for Urdu &#8211; A Resource Poor Language<br />
 102. Michael Roth and Anette Frank. EM-based Alignment of Routes and Route Directions for Natural Language Generation<br />
 103. Lilja Øvrelid, Erik Velldal and Stephan Oepen. Syntactic Scope Resolution in Uncertainty Analysis<br />
 104. Vahed Qazvinian and Dragomir R. Radev. Citation Summarization Through Keyphrase Extraction<br />
 105. Jianfeng Gao, Xiaolong Li, Daniel Micol and Chris Quirk. A Large Scale Ranker-Based System A large scale ranker-bsaed system for search query spelling correction<br />
 106. Fabio Massimo Zanzotto, Ioannis Korkontzelos, Francesca Fallucchi and Suresh Manandhar. Estimating Linear Models for Compositional Distributional Semantics<br />
 107. Daniel Andrade, Tetsuya Nasukawa and Junichi Tsujii. Robust Measurement and Comparison of Context Similarity for Finding Translation Pairs<br />
 108. Markus Dickinson. Generating Learner-Like Morphological Errors in Russian<br />
 109. Alberto Barrón-Cede?o, Paolo Rosso, Eneko Agirre and Gorka Labaka. Plagiarism Detection across Distant Language Pairs<br />
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		<title>条件随机场文献阅读指南</title>
		<link>http://www.52nlp.cn/%e6%9d%a1%e4%bb%b6%e9%9a%8f%e6%9c%ba%e5%9c%ba%e6%96%87%e7%8c%ae%e9%98%85%e8%af%bb%e6%8c%87%e5%8d%97</link>
		<comments>http://www.52nlp.cn/%e6%9d%a1%e4%bb%b6%e9%9a%8f%e6%9c%ba%e5%9c%ba%e6%96%87%e7%8c%ae%e9%98%85%e8%af%bb%e6%8c%87%e5%8d%97#comments</comments>
		<pubDate>Mon, 24 May 2010 15:49:02 +0000</pubDate>
		<dc:creator>52nlp</dc:creator>
				<category><![CDATA[机器学习]]></category>
		<category><![CDATA[条件随机场]]></category>
		<category><![CDATA[自然语言处理]]></category>
		<category><![CDATA[Brown90]]></category>
		<category><![CDATA[CRF]]></category>
		<category><![CDATA[Hanna Wallach]]></category>
		<category><![CDATA[John D. Lafferty]]></category>
		<category><![CDATA[文献]]></category>
		<category><![CDATA[最大熵模型]]></category>

		<guid isPermaLink="false">http://www.52nlp.cn/?p=3378</guid>
		<description><![CDATA[　　与最大熵模型相似，条件随机场（Conditional random fields，CRFs）是一种机器学习模型，在自然语言处理的许多领域（如词性标注、中文分词、命名实体识别等）都有比较好的应用效果。条件随机场最早由John D. Lafferty提出，其也是Brown90的作者之一，和贾里尼克相似，在离开IBM后他去了卡耐基梅隆大学继续搞学术研究，2001年以第一作者的身份发表了CRF的经典论文 “Conditional random fields: Probabilistic models for segmenting and labeling sequence data”。
　　关于条件随机场的参考文献及其他资料，Hanna Wallach在05年整理和维护的这个页面“conditional random fields”非常不错，其中涵盖了自01年CRF提出以来的很多经典论文（不过似乎只到05年，之后并未更新）以及几个相关的工具包(不过也没有包括CRF++），但是仍然非常值得入门条件随机场的读者参考，以下摘选自该网页。
introduction
Conditional random fields (CRFs) are a probabilistic framework for labeling and segmenting structured data, such as sequences, trees and lattices. The underlying idea is that of defining a conditional probability distribution over label sequences given a particular [...]


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			<content:encoded><![CDATA[<p>　　与最大熵模型相似，条件随机场（Conditional random fields，CRFs）是一种机器学习模型，在自然语言处理的许多领域（如词性标注、中文分词、命名实体识别等）都有比较好的应用效果。条件随机场最早由John D. Lafferty提出，其也是<a href="http://www.52nlp.cn/strong-author-team-of-smt-classic-brown90">Brown90</a>的作者之一，和贾里尼克相似，在离开IBM后他去了卡耐基梅隆大学继续搞学术研究，2001年以第一作者的身份发表了CRF的经典论文 “Conditional random fields: Probabilistic models for segmenting and labeling sequence data”。<span id="more-3378"></span><br />
　　关于条件随机场的参考文献及其他资料，Hanna Wallach在05年整理和维护的这个页面“<a href="http://www.inference.phy.cam.ac.uk/hmw26/crf/">conditional random fields</a>”非常不错，其中涵盖了自01年CRF提出以来的很多经典论文（不过似乎只到05年，之后并未更新）以及几个相关的工具包(不过也没有包括CRF++），但是仍然非常值得入门条件随机场的读者参考，以下摘选自该网页。</p>
<h1><a name="introduction">introduction</a></h1>
<p>Conditional random fields (CRFs) are a probabilistic framework for labeling and segmenting structured data, such as sequences, trees and lattices. The underlying idea is that of defining a conditional probability distribution over label sequences given a particular observation sequence, rather than a joint distribution over both label and observation sequences. The primary advantage of CRFs over hidden Markov models is their conditional nature, resulting in the relaxation of the independence assumptions required by HMMs in order to ensure tractable inference. Additionally, CRFs avoid the label bias problem, a weakness exhibited by maximum entropy Markov models (MEMMs) and other conditional Markov models based on directed graphical models. CRFs outperform both MEMMs and HMMs on a number of real-world tasks in many fields, including bioinformatics, computational linguistics and speech recognition.</p>
<h1><a name="tutorial">tutorial</a></h1>
<p>Hanna M. Wallach. <a href="http://www.inference.phy.cam.ac.uk/hmw26/papers/crf_intro.pdf">Conditional Random  Fields: An Introduction.</a> Technical Report MS-CIS-04-21. Department of Computer and Information Science, University of Pennsylvania, 2004.</p>
<h1><a name="papers">papers by year</a></h1>
<h2><a name="2001">2001</a></h2>
<p>John Lafferty, Andrew McCallum, Fernando Pereira. <a href="http://www.cs.umass.edu/%7Emccallum/papers/crf-icml01.ps.gz">Conditional Random  Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.</a> In <em>Proceedings of the Eighteenth International Conference on Machine Learning</em> (ICML-2001), 2001.</p>
<h2><a name="2002">2002</a></h2>
<p>Hanna Wallach. <a href="http://www.cogsci.ed.ac.uk/%7Eosborne/msc-projects/wallach.ps.gz">Efficient Training  of Conditional Random Fields.</a> M.Sc. thesis, Division of Informatics, University of Edinburgh, 2002.</p>
<p>Thomas G. Dietterich. <a href="http://eecs.oregonstate.edu/%7Etgd/publications/mlsd-ssspr.pdf">Machine Learning  for Sequential Data: A Review.</a> In <em>Structural, Syntactic, and Statistical Pattern Recognition; Lecture Notes in Computer Science, Vol. 2396</em>, T. Caelli (Ed.), pp. 15–30, Springer-Verlag, 2002.</p>
<h2><a name="2003">2003</a></h2>
<p>Fei Sha and Fernando Pereira. <a href="http://www.cis.upenn.edu/%7Efeisha/pubs/shallow03.pdf">Shallow Parsing with Conditional Random Fields.</a> In <em>Proceedings of the 2003 Human Language Technology Conference and North American Chapter of the Association for Computational Linguistics</em> (HLT/NAACL-03), 2003.</p>
<p>Andrew McCallum. <a href="http://www.cs.umass.edu/%7Emccallum/papers/ifcrf-uai2003.pdf">Efficiently Inducing  Features of Conditional Random Fields.</a> In <em>Proceedings of the 19th Conference in Uncertainty in Articifical Intelligence</em> (UAI-2003), 2003.</p>
<p>David Pinto, Andrew McCallum, Xing Wei and W. Bruce Croft. <a href="http://www.cs.umass.edu/%7Emccallum/papers/crftable-sigir2003.pdf">Table Extraction  Using Conditional Random Fields.</a> In <em>Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval</em> (SIGIR 2003), 2003.</p>
<p>Andrew McCallum and Wei Li. <a href="http://cnts.uia.ac.be/conll2003/pdf/18891mcc.pdf">Early Results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons.</a> In <em>Proceedings of the Seventh Conference on Natural Language Learning</em> (CoNLL), 2003.</p>
<p>Wei Li and Andrew McCallum. <a href="http://www.cs.umass.edu/%7Emccallum/papers/hindi-talip2003.pdf">Rapid Development  of Hindi Named Entity Recognition Using Conditional Random Fields and Feature Induction.</a> In <em>ACM Transactions on Asian Language Information Processing</em> (TALIP), 2003.</p>
<p>Yasemin Altun and Thomas Hofmann. <a href="http://www.cs.brown.edu/people/altun/pubs/AltunHofmann-EuroSpeech2003.pdf">Large Margin  Methods for Label Sequence Learning.</a> In <em>Proceedings of 8th European Conference on Speech Communication and Technology</em> (EuroSpeech), 2003.</p>
<p>Simon Lacoste-Julien. <a href="http://www.cs.berkeley.edu/%7Eslacoste/school/cs281a/project/M3netReportpdf.pdf">Combining SVM  with graphical models for supervised classification: an introduction to Max-Margin Markov Networks</a>. CS281A Project Report, UC Berkeley, 2003.</p>
<blockquote></blockquote>
<h2><a name="2004">2004</a></h2>
<p>Andrew McCallum, Khashayar Rohanimanesh and Charles Sutton. <a href="http://www.cs.umass.edu/%7Emccallum/papers/dcrf-nips03.pdf">Dynamic Conditional  Random Fields for Jointly Labeling Multiple Sequences.</a> Workshop on Syntax, Semantics, Statistics; 16th Annual Conference on Neural Information Processing Systems (NIPS 2003), 2004.</p>
<p>Kevin Murphy, Antonio Torralba and William T.F. Freeman. <a href="http://web.mit.edu/torralba/www/nips2003.pdf">Using the forest to see the trees: a graphical model relating features, objects and scenes.</a> In <em>Advances in Neural Information Processing Systems 16</em> (NIPS 2003), 2004.</p>
<blockquote></blockquote>
<p>Sanjiv Kumar and Martial Hebert. <a href="http://www-2.cs.cmu.edu/%7Eskumar/DRF/modDRF.pdf">Discriminative Fields for Modeling Spatial Dependencies in Natural Images.</a> In <em>Advances in Neural Information Processing Systems 16</em> (NIPS 2003), 2004.</p>
<p>Ben Taskar, Carlos Guestrin and Daphne Koller. <a href="http://books.nips.cc/papers/files/nips16/NIPS2003_AA04.pdf">Max-Margin Markov  Networks.</a> In <em>Advances in Neural Information Processing Systems 16</em> (NIPS 2003), 2004.</p>
<blockquote></blockquote>
<p>Burr Settles. <a href="http://www.cs.wisc.edu/%7Ebsettles/pub/bsettles-nlpba04.pdf">Biomedical Named  Entity Recognition Using Conditional Random Fields and Rich Feature Sets.</a> To appear in <em>Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications</em> (NLPBA), 2004.</p>
<p>A demo of the system can be downloaded <a href="http://www.cs.wisc.edu/%7Ebsettles/abner/">here</a>.</p>
<p>Charles Sutton, Khashayar Rohanimanesh and Andrew McCallum. <a href="http://www.aicml.cs.ualberta.ca/banff04/icml/pages/papers/308.pdf">Dynamic Conditional  Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data.</a> In <em>Proceedings of the Twenty-First International Conference on Machine Learning</em> (ICML 2004), 2004.</p>
<p>John Lafferty, Xiaojin Zhu and Yan Liu. <a href="http://portal.acm.org/citation.cfm?id=1015330.1015337">Kernel conditional random fields: representation and clique selection.</a> In <em>Proceedings of the Twenty-First International Conference on Machine Learning</em> (ICML 2004), 2004.</p>
<p>Xuming He, Richard Zemel, and Miguel Á. Carreira-Perpiñán. <a href="http://www.cs.toronto.edu/pub/zemel/Papers/cvpr04.pdf">Multiscale conditional random fields for image labelling.</a> In <em>Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</em> (CVPR 2004), 2004.</p>
<p>Yasemin Altun, Alex J. Smola, Thomas Hofmann. <a href="http://www.cs.brown.edu/%7Eth/papers/AltSmoHof-UAI2004.pdf">Exponential Families  for Conditional Random Fields.</a> In <em>Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence</em> (UAI-2004), 2004.</p>
<p>Michelle L. Gregory and Yasemin Altun. <a href="http://www.cs.brown.edu/people/altun/pubs/GregoryAltun.pdf">Using Conditional Random Fields to Predict Pitch Accents in Conversational Speech.</a> In <em>Proceedings of the 42<sup>nd</sup> Annual Meeting of the Association for Computational Linguistics</em> (ACL 2004), 2004.</p>
<p>Brian Roark, Murat Saraclar, Michael Collins and Mark Johnson. <a href="http://www.cslu.ogi.edu/people/roark/ACL04CRFLM.pdf">Discriminative Language  Modeling with Conditional Random Fields and the Perceptron Algorithm.</a> In <em>Proceedings of the 42<sup>nd</sup> Annual Meeting of the Association for Computational Linguistics</em> (ACL 2004), 2004.</p>
<p>Ryan McDonald and Fernando Pereira. <a href="http://www.pdg.cnb.uam.es/BioLINK/workshop_BioCreative_04/handout/pdf/task1A.pdf">Identifying Gene  and Protein Mentions in Text Using Conditional Random Fields.</a> BioCreative, 2004.</p>
<p>Trausti T. Kristjansson, Aron Culotta, Paul Viola and Andrew McCallum.  <a href="http://http//www.cs.umass.edu/%7Emccallum/papers/addrie-aaai04.pdf">Interactive Information  Extraction with Constrained Conditional Random Fields.</a> In <em>Proceedings of the Nineteenth National Conference on Artificial Intelligence</em> (AAAI 2004), 2004.</p>
<p>Thomas G. Dietterich, Adam Ashenfelter and Yaroslav Bulatov. <a href="http://web.engr.oregonstate.edu/%7Etgd/publications/ml2004-treecrf.pdf">Training Conditional  Random Fields via Gradient Tree Boosting.</a> In <em>Proceedings of the Twenty-First International Conference on Machine Learning</em> (ICML 2004), 2004.</p>
<blockquote></blockquote>
<p>John Lafferty, Yan Liu and Xiaojin Zhu. <a href="http://www.aladdin.cs.cmu.edu/papers/pdfs/y2004/kernecon.pdf">Kernel Conditional  Random Fields: Representation, Clique Selection, and Semi-Supervised Learning.</a> Technical Report CMU-CS-04-115, Carnegie Mellon University, 2004.</p>
<p>Fuchun Peng and Andrew McCallum (2004). <a href="http://acl.ldc.upenn.edu/hlt-naacl2004/main/pdf/176_Paper.pdf">Accurate Information  Extraction from Research Papers using Conditional Random Fields.</a> In <em>Proceedings of Human Language Technology Conference and North American Chapter of the Association for Computational Linguistics</em> (HLT/NAACL-04), 2004.</p>
<p>Yasemin Altun, Thomas Hofmann and Alexander J. Smola. <a href="http://www.cs.brown.edu/%7Eth/papers/AltHofSmo-ICML2004.pdf">Gaussian process  classification for segmenting and annotating sequences.</a> In <em>Proceedings of the Twenty-First International Conference on Machine Learning</em> (ICML 2004), 2004.</p>
<p>Yasemin Altun and Thomas Hofmann. <a href="http://www.cs.brown.edu/people/altun/pubs/CS-03-23.ps">Gaussian Process Classification for Segmenting and Annotating Sequences.</a> Technical Report CS-04-12, Department of Computer Science, Brown University, 2004.</p>
<h2><a name="2005">2005</a></h2>
<p>Cristian Smimchisescu, Atul Kanaujia, Zhiguo Li and Dimitris Metaxus. <a href="http://www.cs.toronto.edu/%7Ecrismin/PAPERS/iccv05.pdf">Conditional Models  for Contextual Human Motion Recognition.</a> In <em>Proceedings of the International Conference on Computer Vision</em>, (ICCV 2005), Beijing, China, 2005.</p>
<p>Ariadna Quattoni, Michael Collins and Trevor Darrel. <a href="http://books.nips.cc/papers/files/nips17/NIPS2004_0810.pdf"> Conditional Random Fields for Object Recognition.</a> In <em>Advances in Neural Information Processing Systems 17</em> (NIPS 2004), 2005.</p>
<p>Jospeh Bockhorst and Mark Craven. <a href="http://books.nips.cc/papers/files/nips17/2004_0745.pdf"> Markov Networks for Detecting Overlapping Elements in Sequence Data.</a> In <em>Advances in Neural Information Processing Systems 17</em> (NIPS 2004), 2005.</p>
<p>Antonio Torralba, Kevin P. Murphy, William T. Freeman. <a href="http://www.ai.mit.edu/%7Emurphyk/Papers/BRFaimemo.pdf">Contextual models for object detection using boosted random fields.</a> In <em>Advances in Neural Information Processing Systems 17</em> (NIPS 2004), 2005.</p>
<p>Sunita Sarawagi and William W. Cohen. <a href="http://www-2.cs.cmu.edu/%7Ewcohen/postscript/semiCRF.pdf">Semi-Markov Conditional  Random Fields for Information Extraction.</a> In <em>Advances in Neural Information Processing Systems 17</em> (NIPS 2004), 2005.</p>
<blockquote></blockquote>
<p>Yuan Qi, Martin Szummer and Thomas P. Minka. <a href="http://people.csail.mit.edu/u/a/alanqi/public_html/papers/Qi-Bayesian-CRF-AIstat05.pdf">Bayesian Conditional  Random Fields.</a> To appear in <cite>Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics</cite> (AISTATS 2005), 2005.</p>
<p>Aron Culotta, David Kulp and Andrew McCallum. <a href="http://www.cs.umass.edu/%7Eculotta/pubs/crfgene.pdf">Gene Prediction with Conditional Random Fields.</a> Technical Report UM-CS-2005-028. University of Massachusetts, Amherst, 2005.</p>
<p>Yang Wang and Qiang Ji. <a href="http://www.geocities.com/wang_yang_mr/publication/DCRFcvpr05.pdf">A Dynamic Conditional Random Field Model for Object Segmentation in Image Sequences.</a> In <cite>IEEE Computer Society Conference on Computer Vision and Pattern Recognition</cite> (CVPR 2005), Volume 1, 2005.</p>
<h1><a name="software">software</a></h1>
<p><a href="http://mallet.cs.umass.edu/">MALLET</a>: A Machine Learning for Language Toolkit.</p>
<blockquote><p>MALLET is an integrated collection of Java code useful for statistical natural language processing, document classification, clustering, information extraction, and other machine learning applications to text.</p></blockquote>
<p><a href="http://www.cs.wisc.edu/%7Ebsettles/abner/">ABNER</a>: A Biomedical Named Entity Recognizer.</p>
<blockquote><p>ABNER is a text analysis tool for molecular biology. It is essentially an interactive, user-friendly interface to a system designed as part of the NLPBA/BioNLP 2004 Shared Task challenge.</p></blockquote>
<p><a href="http://minorthird.sourceforge.net/">MinorThird</a>.</p>
<blockquote><p>MinorThird is a collection of Java classes for storing text, annotating text, and learning to extract entities and categorize text.</p></blockquote>
<p><a href="http://www.cs.ubc.ca/%7Emurphyk/Software/CRF/crf.html">Kevin  Murphy&#8217;s MATLAB CRF code</a>.</p>
<blockquote><p>Conditional random fields (chains, trees and general graphs; includes BP code).</p></blockquote>
<p><a href="http://crf.sourceforge.net/">Sunita Sarawagi&#8217;s CRF package</a>.</p>
<blockquote><p>The CRF package is a Java implementation of conditional random fields  for sequential labeling.</p></blockquote>
<p>　　最后推荐<a href="http://crfpp.sourceforge.net/">CRF++:Yet Another CRF toolkit</a>，如果读者对于基于字标注的中文分词感兴趣，可以很快的利用该工具包构造一个基于条件随机场的中文分词工具，而且性能也不赖。</p>
<p>注：转载请注明出处“<a href="http://www.52nlp.cn">我爱自然语言处理</a>”：<a href="http://www.52nlp.cn">www.52nlp.cn</a></p>
<p>本文链接地址：<a href="http://www.52nlp.cn/条件随机场文献阅读指南">http://www.52nlp.cn/条件随机场文献阅读指南</a></p>
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		<title>ACL 2010 Paper国内研究单位录用情况</title>
		<link>http://www.52nlp.cn/acl-2010-paper-%e5%9b%bd%e5%86%85%e7%a0%94%e7%a9%b6%e5%8d%95%e4%bd%8d%e5%bd%95%e7%94%a8%e6%83%85%e5%86%b5</link>
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		<pubDate>Tue, 27 Apr 2010 14:57:17 +0000</pubDate>
		<dc:creator>52nlp</dc:creator>
				<category><![CDATA[自然语言处理]]></category>
		<category><![CDATA[计算语言学]]></category>
		<category><![CDATA[ACL]]></category>
		<category><![CDATA[ACL2010]]></category>
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		<category><![CDATA[刘群]]></category>
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		<category><![CDATA[苏州大学]]></category>
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		<guid isPermaLink="false">http://www.52nlp.cn/?p=3343</guid>
		<description><![CDATA[　　首先感谢几位热心读者对《ACL 2010: List of Accepted Papers》的补充，这里做个总结，如有遗漏和错误之处，欢迎指正。以下是ACL 2010国内研究单位的录用情况：
　　注：所列录用文章并没有区分full paper 和 short paper的录用情况，读者可参考ACL官方网站的录用文章公布清单：List of Accepted Papers，非常感谢neu-nlplab的提醒。
　　
1、中科院计算所刘群老师自然语言处理研究组四篇：
BETTER FILTRATION AND AUGMENTATION FOR HIERARCHICAL PHRASE-BASED TRANSLATION RULES
Zhiyang Wang, Yajuan Lv and Qun Liu
CONSTITUENT-TO-DEPENDENCY TRANSLATION WITH FORESTS
Haitao Mi and Qun Liu
DEPENDENCY PARSING AND PROJECTION BASED ON WORD-PAIR CLASSIFICATION
Wenbin Jiang and Qun Liu
LEARNING LEXICALIZED REORDERING MODELS FROM REORDERING GRAPHS
Jinsong Su, Yang Liu, [...]


相关文章:<ol><li><a href='http://www.52nlp.cn/acl-2010-list-of-accepted-papers' rel='bookmark' title='Permanent Link: ACL 2010: List of Accepted Papers'>ACL 2010: List of Accepted Papers</a></li>
<li><a href='http://www.52nlp.cn/acl09-full-paper-accepted-details' rel='bookmark' title='Permanent Link: ACL09 Full Paper录用情况'>ACL09 Full Paper录用情况</a></li>
<li><a href='http://www.52nlp.cn/coling-2010-list-of-accepted-papers-oral' rel='bookmark' title='Permanent Link: COLING 2010: List of Accepted Papers (Oral)'>COLING 2010: List of Accepted Papers (Oral)</a></li>
<li><a href='http://www.52nlp.cn/acl-ijcnlp-2009-running-two' rel='bookmark' title='Permanent Link: ACL-IJCNLP 2009会议进行时二'>ACL-IJCNLP 2009会议进行时二</a></li>
<li><a href='http://www.52nlp.cn/acl-ijcnlp-2009-running-one' rel='bookmark' title='Permanent Link: ACL-IJCNLP 2009会议进行时一'>ACL-IJCNLP 2009会议进行时一</a></li>
<li><a href='http://www.52nlp.cn/about-acl-anthology-network' rel='bookmark' title='Permanent Link: ACL Anthology 姊妹篇：ACL Anthology Network'>ACL Anthology 姊妹篇：ACL Anthology Network</a></li>
<li><a href='http://www.52nlp.cn/acl-2010%e6%96%87%e7%ab%a0%e5%b7%b2%e5%8f%af%e4%b8%8b%e8%bd%bd' rel='bookmark' title='Permanent Link: ACL 2010文章已可下载'>ACL 2010文章已可下载</a></li>
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<li><a href='http://www.52nlp.cn/acl-ijcnlp-2009-best-paper-awards' rel='bookmark' title='Permanent Link: ACL-IJCNLP 2009 Best Paper Awards'>ACL-IJCNLP 2009 Best Paper Awards</a></li>
<li><a href='http://www.52nlp.cn/moses-recent-developments-and-others' rel='bookmark' title='Permanent Link: Moses近期动态及其他'>Moses近期动态及其他</a></li>
</ol>]]></description>
			<content:encoded><![CDATA[<p>　　首先感谢几位热心读者对《<a href="http://www.52nlp.cn/acl-2010-list-of-accepted-papers">ACL 2010: List of Accepted Papers</a>》的补充，这里做个总结，如有遗漏和错误之处，欢迎指正。以下是ACL 2010国内研究单位的录用情况：<span id="more-3343"></span><br />
　　注：所列录用文章并没有区分full paper 和 short paper的录用情况，读者可参考ACL官方网站的录用文章公布清单：<a href="http://acl2010.org/accepted_papers.html">List of Accepted Papers</a>，非常感谢neu-nlplab的提醒。<br />
　　<br />
<strong>1、中科院计算所刘群老师自然语言处理研究组四篇：</strong></p>
<p>BETTER FILTRATION AND AUGMENTATION FOR HIERARCHICAL PHRASE-BASED TRANSLATION RULES<br />
Zhiyang Wang, Yajuan Lv and Qun Liu</p>
<p>CONSTITUENT-TO-DEPENDENCY TRANSLATION WITH FORESTS<br />
Haitao Mi and Qun Liu</p>
<p>DEPENDENCY PARSING AND PROJECTION BASED ON WORD-PAIR CLASSIFICATION<br />
Wenbin Jiang and Qun Liu</p>
<p>LEARNING LEXICALIZED REORDERING MODELS FROM REORDERING GRAPHS<br />
Jinsong Su, Yang Liu, Yajuan Lv and Qun Liu</p>
<p><strong>2、微软亚洲研究院周明老师自然语言计算组两篇：</strong></p>
<p>A JOINT RULE SELECTION MODEL FOR HIERARCHICAL PHRASE-BASED TRANSLATION（微软亚洲研究院&#038;哈工大）<br />
Lei Cui, Dongdong Zhang, Mu Li, Ming Zhou and Tiejun Zhao</p>
<p>DISCRIMINATIVE PRUNING FOR DISCRIMINATIVE ITG ALIGNMENT<br />
Shujie Liu（哈工大) and Chi-Ho Li（微软亚洲研究院）</p>
<p><strong>3、哈工大两篇：</strong<br />
（注：与微软亚洲研究院的合作文章未算，谢谢xnzhu提醒。）</p>
<p>IMPROVING STATISTICAL MACHINE TRANSLATION WITH MONOLINGUAL COLLOCATION（哈工大）<br />
Zhanyi Liu, Haifeng Wang, Hua Wu and Sheng Li<br />
（注：王海峰老师已去百度，应属于哈工大和东芝的合作文章，谢谢xnzhu提醒。）</p>
<p>MODELING SEMANTIC RELEVANCE FOR QUESTION-ANSWER PAIRS IN WEB SOCIAL COMMUNITIES（哈工大）<br />
Baoxun Wang, Xiaolong Wang, Bingquan Liu, Chengjie Sun and Lin Sun</p>
<p><strong>4、中科院自动化所两篇：</strong></p>
<p>ON JOINTLY RECOGNIZING AND ALIGNING BILINGUAL NAMED ENTITIES（中科院自动化所）<br />
Yufeng Chen, Chengqing Zong and Keh-Yih Su</p>
<p>STRUCTURAL SEMANTIC RELATEDNESS: A KNOWLEDGE-BASED METHOD TO NAMED ENTITY DISAMBIGUATION（中科院自动化所）<br />
Xianpei Han and Jun Zhao</p>
<p><strong>5、苏州大学自然语言处理实验室两篇：</strong></p>
<p>EMPLOYING PERSONAL/IMPERSONAL VIEWS IN SUPERVISED AND SEMI-SUPERVISED SENTIMENT CLASSIFICATION<br />
Shoushan Li, Chu-Ren Huang, Guodong Zhou and Sophia Y. M. </p>
<p>JOINT SYNTACTIC AND SEMANTIC PARSING OF CHINESE（苏州大学）<br />
Junhui Li, Guodong Zhou and Hwee Tou Ng</p>
<p><strong>6、东北大学一篇</strong></p>
<p>BOOSTING-BASED SYSTEM COMBINATION FOR MACHINE TRANSLATION（东北大学）<br />
Tong Xiao, Jingbo Zhu, Muhua Zhu and Huizhen Wang</p>
<p>（注：谢谢neu-nlplab指正）</p>
<p><strong>7、北京大学两篇</strong></p>
<p>CROSS-LANGUAGE DOCUMENT SUMMARIZATION BASED ON MACHINE TRANSLATION QUALITY PREDICTION<br />
Xiaojun Wan（北大）</p>
<p>A SEMI-SUPERVISED KEY PHRASE EXTRACTION APPROACH: LEARNING FROM TITLE PHRASES THROUGH A DOCUMENT SEMANTIC NETWORK<br />
Decong Li, Sujian Li（北大）and Wenjie Li（香港理工大学）<br />
（注：这篇文章似乎属于北大&#038;香港理工大学的合作文章）</p>
<p><strong>8、其他：</strong><br />
COMPARABLE ENTITY MINING FROM COMPARATIVE QUESTIONS<br />
Shasha Li, Chin-Yew Lin, Young-In Song and Zhoujun Li<br />
（国防科大&#038;微软亚洲研究院&#038;北航）</p>
<p>NEWS RECOMMENDATION IN FORUM-BASED SOCIAL MEDIA<br />
Jia Wang, Qing Li, Yuanzhu Peter Chen and Zhangxi Lin<br />
(西南财经大学）</p>
<p>注：转载请注明出处“<a href="http://www.52nlp.cn">我爱自然语言处理</a>”：<a href="http://www.52nlp.cn">www.52nlp.cn</a></p>
<p>本文链接地址：<a href="http://www.52nlp.cn/acl-2010-paper-国内研究单位录用情况">http://www.52nlp.cn/acl-2010-paper-国内研究单位录用情况</a></p>
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<li><a href='http://www.52nlp.cn/acl09-full-paper-accepted-details' rel='bookmark' title='Permanent Link: ACL09 Full Paper录用情况'>ACL09 Full Paper录用情况</a></li>
<li><a href='http://www.52nlp.cn/coling-2010-list-of-accepted-papers-oral' rel='bookmark' title='Permanent Link: COLING 2010: List of Accepted Papers (Oral)'>COLING 2010: List of Accepted Papers (Oral)</a></li>
<li><a href='http://www.52nlp.cn/acl-ijcnlp-2009-running-two' rel='bookmark' title='Permanent Link: ACL-IJCNLP 2009会议进行时二'>ACL-IJCNLP 2009会议进行时二</a></li>
<li><a href='http://www.52nlp.cn/acl-ijcnlp-2009-running-one' rel='bookmark' title='Permanent Link: ACL-IJCNLP 2009会议进行时一'>ACL-IJCNLP 2009会议进行时一</a></li>
<li><a href='http://www.52nlp.cn/about-acl-anthology-network' rel='bookmark' title='Permanent Link: ACL Anthology 姊妹篇：ACL Anthology Network'>ACL Anthology 姊妹篇：ACL Anthology Network</a></li>
<li><a href='http://www.52nlp.cn/acl-2010%e6%96%87%e7%ab%a0%e5%b7%b2%e5%8f%af%e4%b8%8b%e8%bd%bd' rel='bookmark' title='Permanent Link: ACL 2010文章已可下载'>ACL 2010文章已可下载</a></li>
<li><a href='http://www.52nlp.cn/acl-2010-best-paper-awards' rel='bookmark' title='Permanent Link: ACL 2010 Best Paper Awards'>ACL 2010 Best Paper Awards</a></li>
<li><a href='http://www.52nlp.cn/acl-ijcnlp-2009-best-paper-awards' rel='bookmark' title='Permanent Link: ACL-IJCNLP 2009 Best Paper Awards'>ACL-IJCNLP 2009 Best Paper Awards</a></li>
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</ol></p>]]></content:encoded>
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		</item>
		<item>
		<title>ACL 2010: List of Accepted Papers</title>
		<link>http://www.52nlp.cn/acl-2010-list-of-accepted-papers</link>
		<comments>http://www.52nlp.cn/acl-2010-list-of-accepted-papers#comments</comments>
		<pubDate>Mon, 26 Apr 2010 14:22:34 +0000</pubDate>
		<dc:creator>52nlp</dc:creator>
				<category><![CDATA[自然语言处理]]></category>
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		<description><![CDATA[　　ACL会议（Annual Meeting of the Association for Computational Linguistics）是自然语言处理与计算语言学领域最高级别的学术会议，由计算语言学协会主办，每年一届。ACL 2010是第48届年会，将于7月11日~16日在瑞典乌普萨拉举办，由乌普萨拉大学语言学系主办（The 48th Annual Meeting of the Association for Computational Linguistics will be held in Uppsala, Sweden, July 11–16, 2010. The conference will be organized by the Department of Linguistics and Philology at Uppsala University）。
　　今天ACL2010官方网站上列出了今年的full paper录用文章及学生workshop的录用论文，如果读者还记得，去年也稍早一点时间，ACL2009给出了同样的录用文章列表，这里做过记录：ACL09 Full Paper录用情况。以下转载自ACL 2010的官方的“List of Accepted Papers”，我已对部分进行了&#8221;研究单位“的标注，目前已经发现的有中科院计算所刘群老师自然语言处理研究组的四篇，微软亚洲研究院周明老师自然语言计算组两篇，北大两篇，东北大学两篇（?)，欢迎知情的读者继续在这里“爆料”。
A BAYESIAN METHOD FOR ROBUST ESTIMATION OF [...]


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			<content:encoded><![CDATA[<p>　　ACL会议（Annual Meeting of the Association for Computational Linguistics）是自然语言处理与计算语言学领域最高级别的学术会议，由计算语言学协会主办，每年一届。ACL 2010是第48届年会，将于7月11日~16日在瑞典乌普萨拉举办，由乌普萨拉大学语言学系主办（The 48th Annual Meeting of the Association for Computational Linguistics will be held in Uppsala, Sweden, July 11–16, 2010. The conference will be organized by the Department of Linguistics and Philology at Uppsala University）。<br />
　　今天ACL2010官方网站上列出了今年的full paper录用文章及学生workshop的录用论文，如果读者还记得，去年也稍早一点时间，ACL2009给出了同样的录用文章列表，这里做过记录：<a href="http://www.52nlp.cn/acl09-full-paper-accepted-details">ACL09 Full Paper录用情况</a>。以下转载自ACL 2010的官方的“<a href="http://acl2010.org/accepted_papers.html"target=_blank>List of Accepted Papers</a>”，我已对部分进行了&#8221;研究单位“的标注，目前已经发现的有中科院计算所刘群老师自然语言处理研究组的四篇，微软亚洲研究院周明老师自然语言计算组两篇，北大两篇，东北大学两篇（?)，欢迎知情的读者继续在这里“爆料”。<span id="more-3332"></span></p>
<p>A BAYESIAN METHOD FOR ROBUST ESTIMATION OF DISTRIBUTIONAL SIMILARITIES<br />
Jun&#8217;ichi Kazama, Stijn De Saeger, Kow Kuroda, Masaki Murata and Kentaro<br />
Torisawa</p>
<p>A CONSTRAINT PROGRAMMING METHOD FOR DETERMINISTIC BLOOM FILTERING OF CONCEPT LATTICES<br />
Matthew Skala, Victoria Krakovna, János Kramár and Gerald Penn</p>
<p>A GAME-THEORETIC MODEL OF METAPHORICAL BARGAINING<br />
Beata Beigman Klebanov and Eyal Beigman</p>
<p>A HYBRID HIERARCHICAL MODEL FOR MULTI-DOCUMENT SUMMARIZATION<br />
Asli Celikyilmaz and Dilek Hakkani-Tur</p>
<p>A HYBRID RULE/MODEL-BASED FINITE-STATE FRAMEWORK FOR NORMALIZING SMS MESSAGES<br />
Richard Beaufort, Sophie Roekhaut, Louise-Amélie Cougnon and Cédrick<br />
Fairon</p>
<p><strong>A JOINT RULE SELECTION MODEL FOR HIERARCHICAL PHRASE-BASED TRANSLATION（微软亚洲研究院&#038;哈工大）<br />
Lei Cui, Dongdong Zhang, Mu Li, Ming Zhou and Tiejun Zhao</strong></p>
<p>A LATENT DIRICHLET ALLOCATION METHOD FOR SELECTIONAL PREFERENCES<br />
Alan Ritter, Mausam Mausam and Oren Etzioni</p>
<p>A MODEL FOR COMPUTATIONAL DECIPHERMENT<br />
Benjamin Snyder, Regina Barzilay and Kevin Knight</p>
<p>A RATIONAL MODEL OF EYE MOVEMENT CONTROL IN READING<br />
Klinton Bicknell and Roger Levy</p>
<p>A RISK MINIMIZATION FRAMEWORK FOR EXTRACTIVE SPEECH SUMMARIZATION<br />
Shih-Hsiang Lin and Berlin Chen</p>
<p><strong>A SEMI-SUPERVISED KEY PHRASE EXTRACTION APPROACH: LEARNING FROM TITLE PHRASES THROUGH A DOCUMENT SEMANTIC NETWORK<br />
Decong Li, Sujian Li（北大）and Wenjie Li（香港理工大学）</strong></p>
<p>A STRUCTURED MODEL FOR JOINT LEARNING OF ARGUMENT ROLES AND PREDICATE SENSES<br />
Yotaro Watanabe, Masayuki Asahara and Yuji Matsumoto</p>
<p>A STUDY OF INFORMATION RETRIEVAL WEIGHTING SCHEMES FOR SENTIMENT ANALYSIS<br />
Georgios Paltoglou and Mike Thelwall</p>
<p>A TAXONOMY, DATASET, AND CLASSIFIER FOR AUTOMATIC NOUN COMPOUND INTERPRETATION<br />
Stephen Tratz and Eduard Hovy</p>
<p>A TRANSITION-BASED PARSER FOR 2-PLANAR DEPENDENCY STRUCTURES<br />
Carlos Gómez-Rodríguez and Joakim Nivre</p>
<p>A TREE TRANSDUCER MODEL FOR SYNCHRONOUS TREE-ADJOINING GRAMMARS Andreas Maletti</p>
<p><strong>A UNIFIED GRAPH MODEL FOR SENTENCE-BASED OPINION RETRIEVAL（东北大学？）<br />
Binyang Li, Lanjun Zhou, Shi Feng and Kam-Fai Wong</strong></p>
<p>ACCURATE CONTEXT-FREE PARSING WITH COMBINATORY CATEGORIAL GRAMMA<br />
Timothy A. D. Fowler and Gerald Penn</p>
<p>ACTIVE LEARNING-BASED ELICITATION FOR SEMI-SUPERVISED WORD ALIGNMENT<br />
Vamshi Ambati, Stephan Vogel and Jaime Carbonell</p>
<p>ALL WORDS DOMAIN ADAPTED WSD: FINDING A MIDDLE GROUND BETWEEN SUPERVISION AND UNSUPERVISION<br />
Mitesh Khapra, Anup Kulkarni, Saurabh Sohoney and Pushpak Bhattacharyya</p>
<p>ALL WORDS WORD SENSE DISAMBIGUATION: COMBINING ORTHOGONAL MONOLINGUAL AND MULTILINGUAL SOURCES OF EVIDENCE<br />
Weiwei Guo and Mona Diab</p>
<p>AN ACTIVE LEARNING APPROACH TO FINDING RELATED TERMS<br />
David Vickrey, Oscar Kipersztok and Daphne Koller</p>
<p>AN ENTITY-LEVEL APPROACH TO INFORMATION EXTRACTION<br />
Aria Haghighi and Dan Klein</p>
<p>AN EXACT A* METHOD FOR DECIPHERING LETTER-SUBSTITUTION CIPHERS<br />
Eric Corlett and Gerald Penn</p>
<p>AN MDL-INSPIRED OBJECTIVE FUNCTION FOR UNSUPERVISED TRAINING OF GENERATIVE MODELS<br />
Ashish Vaswani, Adam Pauls and David Chiang</p>
<p>ARABIC NAMED ENTITY RECOGNITION: USING FEATURES EXTRACTED FROM NOISY DATA<br />
Yassine Benajiba, Imed Zitouni, Mona Diab and Paolo Rosso</p>
<p>ASSESSING THE ROLE OF DISCOURSE REFERENCES IN ENTAILMENT INFERENCE<br />
Shachar Mirkin, Ido Dagan and Sebastian Padó</p>
<p>AUTHORSHIP ATTRIBUTION USING PROBABILISTIC CONTEXT-FREE GRAMMARS<br />
Sindhu Raghavan, Adriana Kovashka and Raymond Mooney</p>
<p>AUTOMATED PLANNING FOR SITUATED NATURAL LANGUAGE GENERATION<br />
Konstantina Garoufi and Alexander Koller</p>
<p><strong>AUTOMATIC COLLOCATION SUGGESTION IN ACADEMIC WRITING（台湾清华大学）<br />
Jian-Cheng Wu, Yu-Chia Chang and Jason S. Chang</strong></p>
<p>AUTOMATIC EVALUATION METHOD FOR MACHINE TRANSLATION USING NOUN-PHRASE CHUNKING<br />
Hiroshi Echizen-ya and Kenji Araki</p>
<p>AUTOMATIC EVALUATION OF LINGUISTIC QUALITY IN MULTI-DOCUMENT SUMMARIZATION<br />
Emily Pitler, Annie Louis and Ani Nenkova</p>
<p>AUTOMATIC GENERATION OF STORY HIGHLIGHTS<br />
Kristian Woodsend and Mirella Lapata</p>
<p>AUTOMATIC SOCIAL NETWORK EXTRACTION FOR LITERARY FICTION<br />
David Elson, Nicholas Dames and Kathleen McKeown</p>
<p>AUTOMATICALLY GENERATING ANNOTATOR RATIONALES TO IMPROVE SENTIMENT CLASSIFICATION<br />
Ainur Yessenalina, Yejin Choi and Claire Cardie</p>
<p>AUTOMATICALLY GENERATING TERM FREQUENCY INDUCED TAXONOMIES<br />
Karin Murthy, Tanveer Faruquie, L V Subramaniam, Hima Karanam and<br />
Mukesh Mohania</p>
<p>BABELNET: A VERY LARGE MULTILINGUAL SEMANTIC NETWORK<br />
Roberto Navigli and Simone Paolo Ponzetto</p>
<p>BALANCING USER EFFORT AND TRANSLATION ERROR IN INTERACTIVE MACHINE TRANSLATION VIA CONFIDENCE MEASURES<br />
Jesús González Rubio, Daniel Ortiz Martínez and Francisco<br />
Casacuberta</p>
<p><strong>BETTER FILTRATION AND AUGMENTATION FOR HIERARCHICAL PHRASE-BASED TRANSLATION RULES（中科院计算所）<br />
Zhiyang Wang, Yajuan Lv and Qun Liu</strong></p>
<p>BEYOND NOMBANK: A STUDY OF IMPLICIT ARGUMENTATION FOR NOMINAL PREDICATES<br />
Matthew Gerber and Joyce Chai</p>
<p>BILINGUAL LEXICON GENERATION USING NON-ALIGNED SIGNATURES<br />
Daphna Shezaf and Ari Rappoport</p>
<p>BILINGUAL SENSE SIMILARITY FOR STATISTICAL MACHINE TRANSLATION<br />
Boxing Chen, George Foster and Roland Kuhn</p>
<p>BITEXT DEPENDENCY PARSING WITH BILINGUAL SUBTREE CONSTRAINTS<br />
Wenliang Chen, Jun&#8217;ichi Kazama and Kentaro Torisawa</p>
<p>BLOCKED INFERENCE IN BAYESIAN TREE SUBSTITUTION GRAMMARS<br />
Trevor Cohn and Phil Blunsom</p>
<p><strong>BOOSTING-BASED SYSTEM COMBINATION FOR MACHINE TRANSLATION（东北大学）<br />
Tong Xiao, Jingbo Zhu, Muhua Zhu and Huizhen Wang</strong></p>
<p>BOOTSTRAPPING SEMANTIC ANALYZERS FROM NON-CONTRADICTORY TEXTS<br />
Ivan Titov and Mikhail Kozhevnikov</p>
<p>BRIDGING SMT AND TM WITH TRANSLATION RECOMMENDATION<br />
Yifan He, Yanjun Ma, Josef van Genabith and Andy Way</p>
<p>BUCKING THE TREND: LARGE-SCALE COST-FOCUSED ACTIVE LEARNING FOR STATISTICAL MACHINE TRANSLATION<br />
Michael Bloodgood and Chris Callison-Burch</p>
<p>CLASSIFICATION OF FEEDBACK EXPRESSIONS IN MULTIMODAL DATA<br />
Costanza Navarretta and Patrizia Paggio</p>
<p>COGNITIVELY PLAUSIBLE MODELS OF HUMAN LANGUAGE PROCESSING<br />
Frank Keller</p>
<p>COLLOCATION EXTRACTION BEYOND THE INDEPENDENCE ASSUMPTION<br />
Gerlof Bouma</p>
<p>COMBINING DATA AND MATHEMATICAL MODELS OF LANGUAGE CHANGE<br />
Morgan Sonderegger and Partha Niyogi</p>
<p>COMPARABLE ENTITY MINING FROM COMPARATIVE QUESTIONS<br />
Shasha Li, Chin-Yew Lin, Young-In Song and Zhoujun Li</p>
<p>COMPLEXITY ASSUMPTIONS IN ONTOLOGY VERBALISATION<br />
Richard Power</p>
<p>COMPLEXITY METRICS IN AN INCREMENTAL RIGHT-CORNER PARSER<br />
Stephen Wu, Asaf Bachrach, Carlos Cardenas and William Schuler</p>
<p>COMPOSITIONAL MATRIX-SPACE MODELS OF LANGUAGE<br />
Sebastian Rudolph and Eugenie Giesbrecht</p>
<p>COMPUTING WEAKEST READINGS<br />
Alexander Koller and Stefan Thater</p>
<p>CONDITIONAL RANDOM FIELDS FOR WORD HYPHENATION<br />
Nikolaos Trogkanis and Charles Elkan</p>
<p><strong>CONSTITUENT-TO-DEPENDENCY TRANSLATION WITH FORESTS<br />
Haitao Mi and Qun Liu（中科院计算所）</strong></p>
<p>CONTEXTUALIZING SEMANTIC REPRESENTATIONS USING SYNTACTICALLY ENRICHED VECTOR MODELS<br />
Stefan Thater, Hagen Fürstenau and Manfred Pinkal</p>
<p>CONVOLUTION KERNEL OVER PACKED PARSE FOREST<br />
min zhang, hui zhang and haizhou li</p>
<p>COREFERENCE RESOLUTION ACROSS CORPORA: LANGUAGES, CODING SCHEMES, AND PREPROCESSING INFORMATION<br />
Marta Recasens and Eduard Hovy</p>
<p>CORRECTING ERRORS IN A TREEBANK BASED ON SYNCHRONOUS TREE SUBSTITUTION GRAMMAR<br />
Yoshihide Kato and Shigeki Matsubara</p>
<p>CORRECTING ERRORS IN SPEECH RECOGNITION WITH ARTICULATORY DYNAMICS<br />
Frank Rudzicz</p>
<p>CREATING ROBUST SUPERVISED CLASSIFIERS VIA WEB-SCALE N-GRAM DATA<br />
Shane Bergsma, Emily Pitler and Dekang Lin</p>
<p>CROSS LINGUAL ADAPTATION: AN EXPERIMENT ON SENTIMENT CLASSIFICATIONS<br />
bin wei and chris pal<br />
<strong><br />
CROSS-LANGUAGE DOCUMENT SUMMARIZATION BASED ON MACHINE TRANSLATION QUALITY PREDICTION<br />
Xiaojun Wan（北大）</strong></p>
<p>CROSS-LANGUAGE TEXT CLASSIFICATION USING STRUCTURAL CORRESPONDENCE LEARNING<br />
Peter Prettenhofer and Benno Stein</p>
<p>CROSS-LINGUAL LATENT TOPIC EXTRACTION<br />
Duo Zhang, Qiaozhu Mei and ChengXiang Zhai</p>
<p>DATA SPARSENESS IN MACHINE TRANSLATION EVALUATION<br />
Ondřej Bojar, Kamil Kos and David Mareček</p>
<p>DECISION DETECTION USING HIERARCHICAL GRAPHICAL MODELS<br />
Trung H. Bui and Stanley Peters</p>
<p><strong>DEPENDENCY PARSING AND PROJECTION BASED ON WORD-PAIR CLASSIFICATION<br />
Wenbin Jiang and Qun Liu（计算所）</strong></p>
<p>DETECTING ERRORS IN AUTOMATICALLY-PARSED DEPENDENCY RELATIONS<br />
Markus Dickinson</p>
<p>DETECTING EXPERIENCES FROM WEBLOGS<br />
Keun Chan Park, Yoonjae Jeong and Sung Hyon Myaeng</p>
<p>DISCRIMINATIVE MODELING OF EXTRACTION SETS FOR MACHINE TRANSLATION<br />
John DeNero and Dan Klein</p>
<p><strong>DISCRIMINATIVE PRUNING FOR DISCRIMINATIVE ITG ALIGNMENT<br />
Shujie Liu and Chi-Ho Li（微软亚洲研究院）</strong></p>
<p>DISTRIBUTIONAL SIMILARITY VS. PU LEARNING FOR ENTITY SET EXPANSION<br />
Xiao-Li Li, Lei Zhang, Bing Liu and See-Kiong Ng</p>
<p>DIVERSIFY AND COMBINE: IMPROVING WORD ALIGNMENT FOR MACHINE TRANSLATION ON LOW-RESOURCE LANGUAGES<br />
Bing Xiang, Yonggang Deng and Bowen Zhou</p>
<p>DOMAIN ADAPTATION OF MAXIMUM ENTROPY LANGUAGE MODELS<br />
Tanel Alumäe and Mikko Kurimo</p>
<p>DON&#8217;T `HAVE A CLUE&#8217;? UNSUPERVISED CO-LEARNING OF DOWNWARD-ENTAILING OPERATORS.<br />
Cristian Danescu-Niculescu-Mizil and Lillian Lee</p>
<p>DYNAMIC PROGRAMMING FOR LINEAR-TIME SHIFT-REDUCE PARSING<br />
Liang Huang and Kenji Sagae</p>
<p>EFFICIENT INFERENCE THROUGH CASCADES OF WEIGHTED TREE TRANSDUCERS<br />
Jonathan May, Kevin Knight and Heiko Vogler</p>
<p>EFFICIENT PATH COUNTING TRANSDUCERS FOR MINIMUM BAYES-RISK DECODING OF STATISTICAL MACHINE TRANSLATION LATTICES<br />
Graeme Blackwood and William Byrne</p>
<p>EFFICIENT STAGGERED DECODING FOR SEQUENCE LABELING<br />
Nobuhiro Kaji, Yasuhiro Fujiwara, Naoki Yoshinaga and Masaru<br />
Kitsuregawa</p>
<p>EFFICIENT THIRD-ORDER DEPENDENCY PARSERS<br />
Terry Koo and Michael Collins</p>
<p><strong>EMPLOYING PERSONAL/IMPERSONAL VIEWS IN SUPERVISED AND SEMI-SUPERVISED SENTIMENT CLASSIFICATION<br />
Shoushan Li, Chu-Ren Huang, Guodong Zhou and Sophia Y. M. Lee<br />
</strong></p>
<p>ENHANCED WORD DECOMPOSITION BY CALIBRATING THE DECISION THRESHOLD OF COMBINED PROBABILISTIC MODELS<br />
Sebastian Spiegler and Peter Flach</p>
<p>ENTITY-BASED LOCAL COHERENCE MODELLING USING TOPOLOGICAL FIELDS<br />
Jackie Chi Kit Cheung and Gerald Penn</p>
<p>ERROR DETECTION FOR STATISTICAL MACHINE TRANSLATION USING LINGUISTIC FEATURES<br />
Deyi Xiong, Min Zhang and Haizhou Li</p>
<p>ESTIMATING STRICTLY PIECEWISE DISTRIBUTIONS<br />
Jeffrey Heinz and James Rogers</p>
<p>EVALUATING MACHINE TRANSLATIONS USING MNCD<br />
Marcus Dobrinkat, Tero Tapiovaara, Jaakko Väyrynen and Kimmo Kettunen</p>
<p>EVALUATING MULTILANGUAGE-COMPARABILITY OF SUBJECTIVITY ANALYSIS SYSTEMS<br />
Jungi Kim, Jin-ji Li and Jong-Hyeok Lee</p>
<p>EVENT-BASED HYPERSPACE ANALOGUE TO LANGUAGE FOR QUERY EXPANSION<br />
Tingxu Yan, Tamsin Maxwell, Dawei Song, Yuexian Hou and Peng Zhang</p>
<p>EXEMPLAR-BASED MODELS FOR WORD MEANING IN CONTEXT<br />
Katrin Erk and Sebastian Pado</p>
<p>EXPERIMENTS IN GRAPH-BASED SEMI-SUPERVISED LEARNING METHODS FOR CLASS-INSTANCE ACQUISITION<br />
Partha Pratim Talukdar and Fernando Pereira</p>
<p>EXPLORATIONS IN SUBJECT-VERB REORDERING FOR ARABIC-ENGLISH STATISTICAL MACHINE TRANSLATION<br />
Marine Carpuat, Yuval Marton, Nizar Habash and Owen Rambow</p>
<p>EXPLORING SYNTACTIC STRUCTURAL FEATURES FOR SUBTREE ALIGNMENT USING BILINGUAL TREE KERNELS<br />
Jun Sun, Min Zhang and Chew Lim Tan</p>
<p>EXTRACTING SEQUENCES FROM THE WEB<br />
Anthony Fader, Stephen Soderland and Oren Etzioni</p>
<p>EXTRACTION AND APPROXIMATION OF NUMERICAL ATTRIBUTES FROM THE WEB<br />
Dmitry Davidov and Ari Rappoport</p>
<p>FASTER PARSING BY SUPERTAGGER ADAPTATION<br />
Jonathan K. Kummerfeld, Jessika Roesner, Tim Dawborn, James Haggerty,<br />
James R. Curran and Stephen Clark</p>
<p>FILTERING SYNTACTIC CONSTRAINTS FOR SMT<br />
Hailong Cao and Eiichiro Sumita</p>
<p>FINDING COGNATE GROUPS USING PHYLOGENIES<br />
David Hall and Dan Klein</p>
<p>FINE-GRAINED GENRE CLASSIFICATION USING STRUCTURAL LEARNING ALGORITHMS<br />
Zhili Wu, Katja Markert and Serge Sharoff</p>
<p>FINE-GRAINED TREE-TO-STRING TRANSLATION RULE EXTRACTION<br />
Xianchao Wu, Takuya Matsuzaki and Jun&#8217;ichi Tsujii</p>
<p>FIXED LENGTH WORD SUFFIX FOR FACTORED STATISTICAL MACHINE TRANSLATION<br />
Narjes Sharif Razavian and Stephan Vogel</p>
<p>FROM GAZE DURATION AND EYE MOVEMENTS TO ANNOTATION COMPLEXITY &#8212; HOW EYE-TRACKING PROVIDES INSIGHTS INTO ANNOTATION COSTS<br />
Tomanek Katrin, Udo Hahn, Steffen Lohmann and Jürgen Ziegler</p>
<p>FULLY UNSUPERVISED CORE-ADJUNCT ARGUMENT CLASSIFICATION<br />
Omri Abend and Ari Rappoport</p>
<p>FUZZY TREE-TO-TREE TRANSLATION<br />
David Chiang</p>
<p>GENERATING ENTAILMENT RULES FROM FRAMENET<br />
Roni Ben Aharon, Idan Szpektor and Ido Dagan</p>
<p>GENERATING FINE-GRAINED REVIEWS OF SONGS FROM ALBUM REVIEWS<br />
Swati Tata and Barbara Di Eugenio</p>
<p>GENERATING FOCUSED TOPIC-SPECIFIC FOCUSED SENTIMENT LEXICONS<br />
Valentin Jijkoun, Maarten de Rijke and Wouter Weerkamp</p>
<p>GENERATING IMAGE DESCRIPTIONS USING DEPENDENCY RELATIONAL PATTERNS<br />
Ahmet Aker and Robert Gaizauskas</p>
<p><strong>GENERATING TEMPLATES OF ENTITY SUMMARIES WITH AN ENTITY-ASPECT MODEL AND PATTERN MINING ？<br />
Peng Li, Jing Jiang and Yinglin Wang</strong></p>
<p>GLOBAL LEARNING OF FOCUSED ENTAILMENT GRAPHS<br />
Jonathan Berant, Ido Dagan and Jacob Goldberger</p>
<p>HARD CONSTRAINTS FOR GRAMMATICAL FUNCTION LABELLING<br />
Wolfgang Seeker, Ines Rehbein, Jonas Kuhn and Josef Van Genabith</p>
<p>HIERARCHICAL A* PARSING WITH BRIDGE OUTSIDE SCORES<br />
Adam Pauls and Dan Klein</p>
<p>HIERARCHICAL JOINT LEARNING: IMPROVING JOINT PARSING AND NAMED ENTITY RECOGNITION WITH NON-JOINTLY LABELED DATA<br />
Jenny Rose Finkel, Richard Socher and Christopher D. Manning</p>
<p>HIERARCHICAL SEARCH FOR WORD ALIGNMENT<br />
Jason Riesa and Daniel Marcu</p>
<p>HIERARCHICAL SEQUENTIAL LEARNING FOR EXTRACTING OPINIONS AND THEIR ATTRIBUTES<br />
Yejin Choi and Claire Cardie</p>
<p>HINDI-TO-URDU MACHINE TRANSLATION THROUGH TRANSLITERATION<br />
Nadir Durrani, Hassan Sajjad, Alexander Fraser and Helmut Schmid</p>
<p>HOW MANY WORDS IS A PICTURE WORTH? AUTOMATIC CAPTION GENERATION FOR NEWS IMAGES<br />
Yansong Feng and Mirella Lapata</p>
<p>IDENTIFYING GENERIC NOUN PHRASES<br />
Nils Reiter and Anette Frank</p>
<p>IDENTIFYING NON-EXPLICIT CITING SENTENCES FOR CITATION-BASED<br />
SUMMARIZATION.<br />
Vahed Qazvinian and Dragomir R. Radev</p>
<p>IDENTIFYING TEXT POLARITY USING RANDOM WALKS<br />
Ahmed Hassan and Dragomir Radev</p>
<p>IMPORTANCE-DRIVEN TURN-BIDDING FOR SPOKEN DIALOGUE SYSTEMS<br />
Ethan Selfridge and Peter Heeman</p>
<p>IMPROVED UNSUPERVISED POS INDUCTION THROUGH PROTOTYPE DISCOVERY<br />
Omri Abend, Roi Reichart and Ari Rappoport</p>
<p>IMPROVING CHINESE SEMANTIC ROLE LABELING WITH RICH SYNTACTIC FEATURES<br />
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<p>IMPROVING MULTILINGUAL SUMMARIZATION USING A GENETIC ALGORITHM<br />
Marina Litvak, Mark Last and Menahem Friedman</p>
<p><strong>IMPROVING STATISTICAL MACHINE TRANSLATION WITH MONOLINGUAL COLLOCATION（哈工大）<br />
Zhanyi Liu, Haifeng Wang, Hua Wu and Sheng Li</strong></p>
<p>IMPROVING THE USE OF PSEUDO-WORDS FOR EVALUATING SELECTIONAL PREFERENCES<br />
Nathanael Chambers and Dan Jurafsky</p>
<p>INCORPORATING EXTRA-LINGUISTIC INFORMATION INTO REFERENCE RESOLUTION IN COLLABORATIVE TASK DIALOGUE<br />
Ryu Iida, Syunpei Kobayashi and Takenobu Tokunaga</p>
<p>INDUCING DOMAIN-SPECIFIC SEMANTIC CLASS TAGGERS FROM (ALMOST) NOTHING<br />
Ruihong Huang and Ellen Riloff</p>
<p>INDUCTION OF TREE-TO-TREE STSG VIA BAYESIAN INFERENCE AND ITS<br />
APPLICATION TO SENTENCE COMPRESSION<br />
Elif Yamangil</p>
<p>INTELLIGENT SELECTION OF LANGUAGE MODEL TRAINING DATA<br />
Robert C. Moore and Will Lewis</p>
<p>ITERATED SVD-AND-CLUSTERING FOR UNSUPERVISED POS TAGGING<br />
Michael Lamar, Yariv Maron, Mark Johnson and Elie Bienenstock</p>
<p><strong>JOINT SYNTACTIC AND SEMANTIC PARSING OF CHINESE（苏州大学）<br />
Junhui Li, Guodong Zhou and Hwee Tou Ng</strong></p>
<p>JOINTLY OPTIMIZING A TWO-STEP CONDITIONAL RANDOM FIELD MODEL FOR MACHINE TRANSLITERATION AND ITS FAST DECODING ALGORITHM<br />
Dong Yang, Paul Dixon and Sadaoki Furui</p>
<p>KERNEL BASED DISCOURSE ANALYSIS WITH TEMPORAL ORDERING INFORMATION<br />
WenTing WANG, Jian SU and ChewLim TAN</p>
<p>KNOWLEDGE-RICH WORD SENSE DISAMBIGUATION RIVALING SUPERVISED SYSTEMS<br />
Simone Paolo Ponzetto and Roberto Navigli</p>
<p>LAST BUT DEFINITELY NOT LEAST: ON THE ROLE OF THE LAST SENTENCE IN AUTOMATIC SENTIMENT-EXTRACTION<br />
Israela Becker and Vered Aharonson</p>
<p>LATENT VARIABLE MODELS OF SELECTIONAL PREFERENCE<br />
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<p>LEARNING 5000 RELATIONAL EXTRACTORS<br />
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<p>LEARNING ARGUMENTS AND SUPERTYPES OF SEMANTIC RELATIONS USING RECURSIVE PATTERNS<br />
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<p>LEARNING BETTER DATA REPRESENTATION USING INFERENCE-DRIVEN METRIC LEARNING<br />
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<p>LEARNING COMMON GRAMMAR FROM MULTILINGUAL CORPUS<br />
Tomoharu Iwata, Daichi Mochihashi and Hiroshi Sawada</p>
<p><strong>LEARNING LEXICALIZED REORDERING MODELS FROM REORDERING GRAPHS<br />
Jinsong Su, Yang Liu, Yajuan Lv and Qun Liu（中科院计算所）</strong></p>
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<p>LEARNING TO FOLLOW NAVIGATIONAL DIRECTIONS<br />
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<p>LEARNING WORD-CLASS LATTICES FOR DEFINITION AND HYPERNYM EXTRACTION<br />
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<p>LETTER-PHONEME ALIGNMENT: AN EXPLORATION<br />
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<p>METADATA-AWARE MEASURES FOR ANSWER SUMMARIZATION IN COMMUNITY QUESTION ANSWERING<br />
Mattia Tomasoni and Minlie Huang</p>
<p>MINIMIZED MODELS AND GRAMMAR-INFORMED INITIALIZATION FOR SUPERTAGGING WITH HIGHLY AMBIGUOUS LEXICONS<br />
Sujith Ravi, Jason Baldridge and Kevin Knight</p>
<p>MODELING NORMS OF TURN-TAKING IN MULTI-PARTY CONVERSATION<br />
Kornel Laskowski</p>
<p><strong>MODELING SEMANTIC RELEVANCE FOR QUESTION-ANSWER PAIRS IN WEB SOCIAL COMMUNITIES（哈工大）<br />
Baoxun Wang, Xiaolong Wang, Bingquan Liu, Chengjie Sun and Lin Sun</strong></p>
<p>MODELS OF METAPHOR IN NLP<br />
Ekaterina Shutova</p>
<p>MULTILINGUAL RELEVANCE FEEDBACK: ONE LANGUAGE CAN HELP ANOTHER<br />
Manoj Kumar Chinnakotla, Karthik Raman and Pushpak Bhattacharyya</p>
<p>NEWS RECOMMENDATION IN FORUM-BASED SOCIAL MEDIA<br />
Jia Wang, Qing Li, Yuanzhu Peter Chen and Zhangxi Lin</p>
<p>NOW, WHERE WAS I? RESUMPTION STRATEGIES FOR AN IN-VEHICLE DIALOGUE SYSTEM<br />
Jessica Villing</p>
<p><strong>ON JOINTLY RECOGNIZING AND ALIGNING BILINGUAL NAMED ENTITIES（中科院自动化所）<br />
Yufeng Chen, Chengqing Zong and Keh-Yih Su</strong></p>
<p>ON LEARNING SUBTYPES OF THE PART-WHOLE RELATION: DO NOT MIX YOUR SEEDS ASHWIN ITTOO and GOSSE BOUMA</p>
<p>ON THE COMPUTATIONAL COMPLEXITY OF DOMINANCE LINKS IN GRAMMATICAL FORMALISMS<br />
Sylvain Schmitz</p>
<p>ONLINE GENERATION OF LOCALITY SENSITIVE HASH SIGNATURES<br />
Benjamin Van Durme and Ashwin Lall</p>
<p>OPEN INFORMATION EXTRACTION USING WIKIPEDIA<br />
Fei Wu and Daniel S. Weld</p>
<p>OPEN-DOMAIN SEMANTIC ROLE LABELING BY LEARNING MODELS OF WORD SPANS<br />
Fei Huang and Alexander Yates</p>
<p>OPTIMAL RANK REDUCTION OF LCFRSS WITH FAN-OUT 2<br />
Benoît Sagot and Giorgio Satta</p>
<p>OPTIMISING INFORMATION PRESENTATION FOR SPOKEN DIALOGUE SYSTEMS<br />
Verena Rieser, Oliver Lemon and Xingkun Liu</p>
<p>OPTIMIZING INFORMATIVENESS AND READABILITY FOR SENTIMENT SUMMARIZATION<br />
Hitoshi Nishikawa, Takaaki Hasegawa, Yoshihiro Matsuo and Genichiro<br />
Kikui</p>
<p>OPTIMIZING QUESTION ANSWERING ACCURACY BY MAXIMIZING LOG-LIKELIHOOD<br />
Matthias H. Heie, Edward W. D. Whittaker and Sadaoki Furui</p>
<p>PARAPHRASE LATTICE FOR STATISTICAL MACHINE TRANSLATION<br />
Takashi Onishi, Masao Utiyama and Eiichiro Sumita</p>
<p>PCFGS, TOPIC MODELS, ADAPTOR GRAMMARS AND LEARNING TOPICAL COLLOCATIONS AND THE STRUCTURE OF PROPER NAMES<br />
Mark Johnson</p>
<p>PHRASE-BASED STATISTICAL LANGUAGE GENERATION USING GRAPHICAL MODELS AND ACTIVE LEARNING<br />
Francois Mairesse, Milica Gasic, Filip Jurcicek, Simon Keizer, Jorge<br />
Prombonas, Blaise Thomson, Kai Yu and Steve Young</p>
<p>PHYLOGENETIC GRAMMAR INDUCTION<br />
Taylor Berg-Kirkpatrick and Dan Klein</p>
<p>PLOT INDUCTION AND EVOLUTIONARY SEARCH FOR STORY GENERATION<br />
Neil McIntyre and Mirella Lapata</p>
<p>PRACTICAL VERY LARGE SCALE CRFS<br />
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<p>PREDICATE ARGUMENT STRUCTURE ANALYSIS USING TRANSFORMATION BASED LEARNING<br />
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<p>PREFERENCES VERSUS ADAPTATION DURING REFERRING EXPRESSION GENERATION<br />
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<p>PROFITING FROM MARK-UP: HYPER-TEXT ANNOTATIONS FOR GUIDED PARSING<br />
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<p>PSEUDO-WORD FOR PHRASE-BASED MACHINE TRANSLATION<br />
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<p>READING BETWEEN THE LINES: LEARNING TO MAP HIGH-LEVEL INSTRUCTIONS TO COMMANDS<br />
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<p>REBANKING: GRAMMAR ENGINEERING FOR A STATISTICAL PARSER<br />
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<p>RECONCILE: A COREFERENCE RESOLUTION RESEARCH PLATFORM<br />
Veselin Stoyanov, Claire Cardie, Nathan Gilbert, Ellen Riloff, David<br />
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<p>SEMANTICS-DRIVEN SHALLOW PARSING FOR CHINESE SEMANTIC ROLE LABELING<br />
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<p>SENTENCE AND EXPRESSION LEVEL ANNOTATION OF OPINIONS IN USER-GENERATED DISCOURSE<br />
Cigdem Toprak, Niklas Jakob and Iryna Gurevych</p>
<p>SENTIMENT LEARNING ON PRODUCT REVIEWS VIA SENTIMENT ONTOLOGY TREE<br />
Wei Wei and Jon Atle Gulla</p>
<p>SIMPLE SEMI-SUPERVISED TRAINING OF PART-OF-SPEECH TAGGERS<br />
Anders Søgaard</p>
<p>SIMPLE, ACCURATE PARSING WITH AN ALL-FRAGMENTS GRAMMAR<br />
Mohit Bansal and Dan Klein</p>
<p>SIMULTANEOUS TOKENIZATION AND PART-OF-SPEECH TAGGING FOR ARABIC WITHOUT A MORPHOLOGICAL ANALYZER<br />
Seth Kulick</p>
<p>SPARSITY IN DEPENDENCY GRAMMAR INDUCTION<br />
Jennifer Gillenwater, Kuzman Ganchev, João Graça, Ben Taskar and<br />
Fernando Pereira</p>
<p>STARTING FROM SCRATCH IN SEMANTIC ROLE LABELING<br />
Michael Connor, Cynthia Fisher and Dan Roth</p>
<p>STRING EXTENSION LEARNING<br />
Jeffrey Heinz<br />
<strong><br />
STRUCTURAL SEMANTIC RELATEDNESS: A KNOWLEDGE-BASED METHOD TO NAMED ENTITY DISAMBIGUATION（中科院自动化所）<br />
Xianpei Han and Jun Zhao</strong></p>
<p>SUPERVISED NOUN PHRASE COREFERENCE RESEARCH: THE FIRST FIFTEEN YEARS<br />
Vincent Ng</p>
<p>SYNTACTIC AND SEMANTIC FACTORS IN PROCESSING DIFFICULTY: AN INTEGRATED MEASURE<br />
Jeff Mitchell, Mirella Lapata, Vera Demberg and Frank Keller</p>
<p>SYNTAX-TO-MORPHOLOGY MAPPING IN FACTORED PHRASE-BASED STATISTICAL MACHINE TRANSLATION FROM ENGLISH TO TURKISH<br />
Reyyan Yeniterzi and Kemal Oflazer</p>
<p>SYSTEMX: AN ALGEBRAIC APPROACH TO DECLARATIVE INFORMATION EXTRACTION<br />
Laura Chiticariu, Rajasekar Krishnamurthy, Yunyao Li, Sriram Raghavan,<br />
Frederick Reiss and Shivakumar Vaithyanathan</p>
<p>TEMPORAL INFORMATION PROCESSING OF A NEW LANGUAGE: FAST PORTING WITH MINIMAL RESOURCES<br />
Francisco Costa and António Branco</p>
<p>THE IMPACT OF INTERPRETATION PROBLEMS ON TUTORIAL DIALOGUE<br />
Myroslava Dzikovska, Johanna Moore, Natalie Steinhauser and Gwendolyn Campbell</p>
<p>THE IMPORTANCE OF RULE RESTRICTIONS IN CCG<br />
Marco Kuhlmann, Alexander Koller and Giorgio Satta</p>
<p>THE INFLUENCE OF DISCOURSE ON SYNTAX: A PSYCHOLINGUISTIC MODEL OF SENTENCE PROCESSING<br />
Amit Dubey</p>
<p>THE MANUALLY ANNOTATED SUB-CORPUS: A COMMUNITY RESOURCE FOR AND BY THE PEOPLE<br />
Nancy Ide, Collin Baker, Christiane Fellbaum and Rebecca Passonneau</p>
<p>THE PREVALENCE OF DESCRIPTIVE REFERRING EXPRESSIONS IN NEWS AND NARRATIVE<br />
Raquel Hervas and Mark Finlayson</p>
<p>THE SAME-HEAD HEURISTIC FOR COREFERENCE<br />
Micha Elsner and Eugene Charniak</p>
<p>TOP-DOWN K-BEST A* PARSING<br />
Adam Pauls, Dan Klein and Chris Quirk</p>
<p>TOPIC MODELS FOR WORD SENSE DISAMBIGUATION AND TOKEN-BASED IDIOM DETECTION<br />
Linlin Li, Benjamin Roth and Caroline Sporleder</p>
<p>TOWARDS OPEN-DOMAIN SEMANTIC ROLE LABELING<br />
Danilo Croce, Cristina Giannone, Paolo Annesi and Roberto Basili</p>
<p>TOWARDS ROBUST MULTI-TOOL TAGGING. AN OWL/DL-BASED APPROACH<br />
Christian Chiarcos</p>
<p>TRAINING PHRASE TRANSLATION MODELS WITH LEAVING-ONE-OUT<br />
Joern Wuebker, Arne Mauser and Hermann Ney</p>
<p>TREE-BASED DETERMINISTIC DEPENDENCY PARSING — AN APPLICATION TO NIVRE&#8217;S METHOD —<br />
Kotaro Kitagawa and Kumiko Tanaka-Ishii</p>
<p>TRUSTRANK: INDUCING TRUST IN AUTOMATIC TRANSLATIONS VIA RANKING Radu Soricut and Abdessamad Echihabi</p>
<p>UNDERSTANDING THE SEMANTIC STRUCTURE OF NOUN PHRASE QUERIES<br />
Xiao Li</p>
<p>UNSUPERVISED DISCOURSE SEGMENTATION OF DOCUMENTS WITH INHERENTLY PARALLEL STRUCTURE<br />
Minwoo Jeong and Ivan Titov</p>
<p>UNSUPERVISED EVENT COREFERENCE RESOLUTION WITH RICH LINGUISTIC FEATURES<br />
Cosmin Bejan and Sanda Harabagiu</p>
<p>UNSUPERVISED ONTOLOGY INDUCTION FROM TEXT<br />
Hoifung Poon and Pedro Domingos</p>
<p>UNTANGLING THE CROSS-LINGUAL LINK STRUCTURE OF WIKIPEDIA<br />
Gerard de Melo and Gerhard Weikum</p>
<p>USING ANAPHORA RESOLUTION TO IMPROVE OPINION TARGET IDENTIFICATION IN MOVIE REVIEWS<br />
Niklas Jakob and Iryna Gurevych</p>
<p>USING DOCUMENT LEVEL CROSS-EVENT INFERENCE TO IMPROVE EVENT EXTRACTION<br />
Shasha Liao and Ralph Grishman</p>
<p>USING PARSE FEATURES FOR PREPOSITION SELECTION AND ERROR DETECTION<br />
Joel Tetreault, Jennifer Foster and Martin Chodorow</p>
<p>USING SMALLER CONSTITUENTS RATHER THAN SENTENCES IN ACTIVE LEARNING FOR JAPANESE DEPENDENCY PARSING<br />
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<p>USING SPEECH TO REPLY TO SMS MESSAGES WHILE DRIVING: AN IN-CAR USER STUDY<br />
Yun-Cheng Ju and Tim Paek</p>
<p>VITERBI TRAINING FOR PCFGS: HARDNESS RESULTS AND COMPETITIVENESS OF UNIFORM INITIALIZATION<br />
Shay Cohen and Noah A Smith</p>
<p>VOCABULARY CHOICE AS AN INDICATOR OF PERSPECTIVE<br />
Beata Beigman Klebanov, Eyal Beigman and Daniel Diermeier</p>
<p>WIKIPEDIA AS SENSE INVENTORY TO IMPROVE DIVERSITY IN WEB SEARCH RESULTS<br />
Celina Santamaría, Julio Gonzalo and Javier Artiles</p>
<p>WORD ALIGNMENT WITH SYNONYM REGULARIZATION<br />
Hiroyuki Shindo, Akinori Fujino and Masaaki Nagata</p>
<p>WORD REPRESENTATIONS: A SIMPLE AND GENERAL METHOD FOR SEMI-SUPERVISED LEARNING<br />
Joseph Turian, Lev-Arie Ratinov and Yoshua Bengio</p>
<p>WRAPPING UP A SUMMARY: FROM REPRESENTATION TO GENERATION<br />
Mijail Kabadjov, Josef Steinberger, Marco Turchi and Ralf Steinberger</p>
<p>“ASK NOT WHAT TEXTUAL ENTAILMENT CAN DO FOR YOU&#8230;”<br />
Dan Roth, Mark Sammons and V.G.Vinod Vydiswaran</p>
<p>“WAS IT GOOD? IT WAS PROVOCATIVE.” LEARNING THE MEANING OF SCALAR ADJECTIVES<br />
Marie-Catherine de Marneffe, Christopher Potts and Christopher D.<br />
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<p>注：转载请注明出处“<a href="http://www.52nlp.cn">我爱自然语言处理</a>”：<a href="http://www.52nlp.cn">www.52nlp.cn</a></p>
<p>本文链接地址：<a href="http://www.52nlp.cn/acl-2010-list-of-accepted-papers">http://www.52nlp.cn/acl-2010-list-of-accepted-papers</a></p>
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		</item>
		<item>
		<title>《自然语言处理的形式模型》导读</title>
		<link>http://www.52nlp.cn/%e8%87%aa%e7%84%b6%e8%af%ad%e8%a8%80%e5%a4%84%e7%90%86%e7%9a%84%e5%bd%a2%e5%bc%8f%e6%a8%a1%e5%9e%8b-%e5%af%bc%e8%af%bb</link>
		<comments>http://www.52nlp.cn/%e8%87%aa%e7%84%b6%e8%af%ad%e8%a8%80%e5%a4%84%e7%90%86%e7%9a%84%e5%bd%a2%e5%bc%8f%e6%a8%a1%e5%9e%8b-%e5%af%bc%e8%af%bb#comments</comments>
		<pubDate>Thu, 22 Apr 2010 16:11:40 +0000</pubDate>
		<dc:creator>52nlp</dc:creator>
				<category><![CDATA[自然语言处理]]></category>
		<category><![CDATA[转载]]></category>
		<category><![CDATA[冯志伟]]></category>
		<category><![CDATA[自然语言处理书籍]]></category>
		<category><![CDATA[自然语言处理的形式模型]]></category>

		<guid isPermaLink="false">http://www.52nlp.cn/?p=3325</guid>
		<description><![CDATA[　　前几天在这里介绍过冯志伟老师的“自然语言处理的形式模型“，wibe同学第一时间在卓越购得此书，并且很快得写了“《自然语言处理的形式模型》导读“这篇书评。读了一下，感觉写得不错，就在这里转载了，方便有需求的读者作一些参考。
　　　　　　　　《自然语言处理的形式模型》导读
　　　　　　　　　　　作者：王增才(wibe)
　　　　　　　　　　　邮箱：wangzengcai@126.com
　　该书将自然语言处理的方法分为两种：理性主义（基于规则的方法）方法与经验主义（基于统计的方法）。该书对自然语言处理中的很多种形式模型进行了系统的介绍。基于规则的形式模型，介绍了短语结构语法，递归转移网络等等；基于统计的形式模型，介绍了Markov链，概率语法，Bayes公式，HMM等等。
　　该书介绍了很多种主流的形式模型，在一定程度上反映了国内外自然语言处理方面的成果，可以作为一本入门书或者工具书来使用，有助于我们大体把握自然语言处理发展动向的。
　　该书是手册性的综合概述书籍，有如下优点：
　　1.介绍了很多种规则和统计的形式模型。
　　2.简练的论述了形式模型的优缺点。
　　3.该书各章写作风格一致，内容协调，特别适合对自然语言处理感兴趣和刚入门的朋友们阅读。
　　缺点：
　　1.数学公式较多，文科背景的朋友们阅读和理解起来可能会有一些困难。
　　2.没有论述基于模糊数学的自然语言处理的形式模型。不知道是冯老师不熟悉这块，还是有意回避。据我了解，冯老师本人是擅长于基于统计的形式模型研究的。我国的学者伍铁平（代表作《模糊语言学》）与张乔老师（代表作《模糊语义学》）等等对模糊语言学颇有研究。
　　据我所了解，该书是国内第一本综述基于规则与统计的自然语言处理方法的书籍，是值得一读的。不推荐想深究某种具体算法的朋友阅读该书。深究算法，还是阅读原作者的论文比较好。
参考资料
1.《自然语言处理的形式模型》，冯志伟，中国科学技术大学出版社，2010年01月
转自作者新浪博客：http://blog.sina.com.cn/s/blog_633e67d10100i5pl.html
卓越亚马逊：自然语言处理的形式模型
注：转载请注明出处“我爱自然语言处理”：www.52nlp.cn
本文链接地址：http://www.52nlp.cn/自然语言处理的形式模型-导读










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			<content:encoded><![CDATA[<p>　　前几天在这里介绍过冯志伟老师的“<a href="http://www.52nlp.cn/%E5%86%AF%E5%BF%97%E4%BC%9F-%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E7%9A%84%E5%BD%A2%E5%BC%8F%E6%A8%A1%E5%9E%8B">自然语言处理的形式模型</a>“，wibe同学第一时间在卓越购得此书，并且很快得写了“<a href="http://blog.sina.com.cn/s/blog_633e67d10100i5pl.html"target=_blank>《自然语言处理的形式模型》导读</a>“这篇书评。读了一下，感觉写得不错，就在这里转载了，方便有需求的读者作一些参考。<span id="more-3325"></span></p>
<p>　　　　　　　　<strong>《自然语言处理的形式模型》导读</strong><br />
　　　　　　　　　　　作者：王增才(wibe)<br />
　　　　　　　　　　　邮箱：wangzengcai@126.com</p>
<p>　　该书将自然语言处理的方法分为两种：理性主义（基于规则的方法）方法与经验主义（基于统计的方法）。该书对自然语言处理中的很多种形式模型进行了系统的介绍。基于规则的形式模型，介绍了短语结构语法，递归转移网络等等；基于统计的形式模型，介绍了Markov链，概率语法，Bayes公式，HMM等等。</p>
<p>　　该书介绍了很多种主流的形式模型，在一定程度上反映了国内外自然语言处理方面的成果，可以作为一本入门书或者工具书来使用，有助于我们大体把握自然语言处理发展动向的。</p>
<p>　　该书是手册性的综合概述书籍，有如下优点：</p>
<p>　　1.介绍了很多种规则和统计的形式模型。</p>
<p>　　2.简练的论述了形式模型的优缺点。</p>
<p>　　3.该书各章写作风格一致，内容协调，特别适合对自然语言处理感兴趣和刚入门的朋友们阅读。</p>
<p>　　缺点：</p>
<p>　　1.数学公式较多，文科背景的朋友们阅读和理解起来可能会有一些困难。</p>
<p>　　2.没有论述基于模糊数学的自然语言处理的形式模型。不知道是冯老师不熟悉这块，还是有意回避。据我了解，冯老师本人是擅长于基于统计的形式模型研究的。我国的学者伍铁平（代表作《模糊语言学》）与张乔老师（代表作《模糊语义学》）等等对模糊语言学颇有研究。</p>
<p>　　据我所了解，该书是国内第一本综述基于规则与统计的自然语言处理方法的书籍，是值得一读的。不推荐想深究某种具体算法的朋友阅读该书。深究算法，还是阅读原作者的论文比较好。</p>
<p>参考资料</p>
<p>1.《自然语言处理的形式模型》，冯志伟，中国科学技术大学出版社，2010年01月</p>
<p>转自作者新浪博客：<a href="http://blog.sina.com.cn/s/blog_633e67d10100i5pl.html"target=_blank>http://blog.sina.com.cn/s/blog_633e67d10100i5pl.html</a></p>
<p>卓越亚马逊：<a href="http://www.amazon.cn/mn/searchApp?source=garypyang-23&#038;searchType=1&#038;keywords=自然语言处理的形式模型" title="自然语言处理的形式模型">自然语言处理的形式模型</a></p>
<p>注：转载请注明出处“<a href="http://www.52nlp.cn">我爱自然语言处理</a>”：<a href="http://www.52nlp.cn">www.52nlp.cn</a></p>
<p>本文链接地址：<a href="http://www.52nlp.cn/自然语言处理的形式模型-导读">http://www.52nlp.cn/自然语言处理的形式模型-导读</a></p>
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