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Enriching Context Information for Entity Linking with Web Data
Entity linking (EL) is the task of determining the identity of textual entity mentions given a predefined knowledge base (KB). Plenty of existing efforts have been made on this task using either “local” information (contextual information of the mention in the text), or “global” information (relatio...
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Published in: | Journal of computer science and technology 2020-07, Vol.35 (4), p.724-738 |
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description | Entity linking (EL) is the task of determining the identity of textual entity mentions given a predefined knowledge base (KB). Plenty of existing efforts have been made on this task using either “local” information (contextual information of the mention in the text), or “global” information (relations among candidate entities). However, either local or global information might be insufficient especially when the given text is short. To get richer local and global information for entity linking, we propose to enrich the context information for mentions by getting extra contexts from the web through web search engines (WSE). Based on the intuition above, two novel attempts are made. The first one adds web-searched results into an embedding-based method to expand the mention’s local information, where we try two different methods to help generate high-quality web contexts: one is to apply the attention mechanism and the other is to use the abstract extraction method. The second one uses the web contexts to extend the global information, i.e., finding and utilizing more extra relevant mentions from the web contexts with a graph-based model. Finally, we combine the two models we propose to use both extended local and global information from the extra web contexts. Our empirical study based on six real-world datasets shows that using extra web contexts to extend the local and the global information could effectively improve the
F
1 score of entity linking. |
doi_str_mv | 10.1007/s11390-020-0280-1 |
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F
1 score of entity linking.</description><identifier>ISSN: 1000-9000</identifier><identifier>EISSN: 1860-4749</identifier><identifier>DOI: 10.1007/s11390-020-0280-1</identifier><language>eng</language><publisher>Singapore: Springer Singapore</publisher><subject>Analysis ; Artificial Intelligence ; Computational linguistics ; Computer Science ; Context ; Data Structures and Information Theory ; Database searching ; Information Systems Applications (incl.Internet) ; Internet/Web search services ; Knowledge bases (artificial intelligence) ; Language processing ; Methods ; Natural language interfaces ; Online searching ; Regular Paper ; Search engines ; Software Engineering ; Theory of Computation ; Towns ; Web services</subject><ispartof>Journal of computer science and technology, 2020-07, Vol.35 (4), p.724-738</ispartof><rights>Institute of Computing Technology, Chinese Academy of Sciences 2020</rights><rights>COPYRIGHT 2020 Springer</rights><rights>Institute of Computing Technology, Chinese Academy of Sciences 2020.</rights><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c435t-81eb22ea78671d0b3cd29d1690e2772880bb54736bd8cca5cf3db4767c1ffc4e3</citedby><cites>FETCH-LOGICAL-c435t-81eb22ea78671d0b3cd29d1690e2772880bb54736bd8cca5cf3db4767c1ffc4e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.wanfangdata.com.cn/images/PeriodicalImages/jsjkxjsxb-e/jsjkxjsxb-e.jpg</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2918579367?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>315,786,790,11715,27957,27958,36095,44398</link.rule.ids></links><search><creatorcontrib>Wang, Yi-Ting</creatorcontrib><creatorcontrib>Shen, Jie</creatorcontrib><creatorcontrib>Li, Zhi-Xu</creatorcontrib><creatorcontrib>Yang, Qiang</creatorcontrib><creatorcontrib>Liu, An</creatorcontrib><creatorcontrib>Zhao, Peng-Peng</creatorcontrib><creatorcontrib>Xu, Jia-Jie</creatorcontrib><creatorcontrib>Zhao, Lei</creatorcontrib><creatorcontrib>Yang, Xun-Jie</creatorcontrib><title>Enriching Context Information for Entity Linking with Web Data</title><title>Journal of computer science and technology</title><addtitle>J. Comput. Sci. Technol</addtitle><description>Entity linking (EL) is the task of determining the identity of textual entity mentions given a predefined knowledge base (KB). Plenty of existing efforts have been made on this task using either “local” information (contextual information of the mention in the text), or “global” information (relations among candidate entities). However, either local or global information might be insufficient especially when the given text is short. To get richer local and global information for entity linking, we propose to enrich the context information for mentions by getting extra contexts from the web through web search engines (WSE). Based on the intuition above, two novel attempts are made. The first one adds web-searched results into an embedding-based method to expand the mention’s local information, where we try two different methods to help generate high-quality web contexts: one is to apply the attention mechanism and the other is to use the abstract extraction method. The second one uses the web contexts to extend the global information, i.e., finding and utilizing more extra relevant mentions from the web contexts with a graph-based model. Finally, we combine the two models we propose to use both extended local and global information from the extra web contexts. Our empirical study based on six real-world datasets shows that using extra web contexts to extend the local and the global information could effectively improve the
F
1 score of entity linking.</description><subject>Analysis</subject><subject>Artificial Intelligence</subject><subject>Computational linguistics</subject><subject>Computer Science</subject><subject>Context</subject><subject>Data Structures and Information Theory</subject><subject>Database searching</subject><subject>Information Systems Applications (incl.Internet)</subject><subject>Internet/Web search services</subject><subject>Knowledge bases (artificial intelligence)</subject><subject>Language processing</subject><subject>Methods</subject><subject>Natural language interfaces</subject><subject>Online searching</subject><subject>Regular Paper</subject><subject>Search engines</subject><subject>Software Engineering</subject><subject>Theory of Computation</subject><subject>Towns</subject><subject>Web 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Comput. Sci. Technol</stitle><date>2020-07-01</date><risdate>2020</risdate><volume>35</volume><issue>4</issue><spage>724</spage><epage>738</epage><pages>724-738</pages><issn>1000-9000</issn><eissn>1860-4749</eissn><abstract>Entity linking (EL) is the task of determining the identity of textual entity mentions given a predefined knowledge base (KB). Plenty of existing efforts have been made on this task using either “local” information (contextual information of the mention in the text), or “global” information (relations among candidate entities). However, either local or global information might be insufficient especially when the given text is short. To get richer local and global information for entity linking, we propose to enrich the context information for mentions by getting extra contexts from the web through web search engines (WSE). Based on the intuition above, two novel attempts are made. The first one adds web-searched results into an embedding-based method to expand the mention’s local information, where we try two different methods to help generate high-quality web contexts: one is to apply the attention mechanism and the other is to use the abstract extraction method. The second one uses the web contexts to extend the global information, i.e., finding and utilizing more extra relevant mentions from the web contexts with a graph-based model. Finally, we combine the two models we propose to use both extended local and global information from the extra web contexts. Our empirical study based on six real-world datasets shows that using extra web contexts to extend the local and the global information could effectively improve the
F
1 score of entity linking.</abstract><cop>Singapore</cop><pub>Springer Singapore</pub><doi>10.1007/s11390-020-0280-1</doi><tpages>15</tpages></addata></record> |
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subjects | Analysis Artificial Intelligence Computational linguistics Computer Science Context Data Structures and Information Theory Database searching Information Systems Applications (incl.Internet) Internet/Web search services Knowledge bases (artificial intelligence) Language processing Methods Natural language interfaces Online searching Regular Paper Search engines Software Engineering Theory of Computation Towns Web services |
title | Enriching Context Information for Entity Linking with Web Data |
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