An approach for measuring semantic similarity between words using multiple information sources
Semantic similarity between words is becoming a generic problem for many applications of computational linguistics and artificial intelligence. This paper explores the determination of semantic similarity by a number of information sources, which consist of structural semantic information from a lex...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on knowledge and data engineering 2003-07, Vol.15 (4), p.871-882 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 882 |
|---|---|
| container_issue | 4 |
| container_start_page | 871 |
| container_title | IEEE transactions on knowledge and data engineering |
| container_volume | 15 |
| creator | Li, Y. Bandar, Z.A. Mclean, D. |
| description | Semantic similarity between words is becoming a generic problem for many applications of computational linguistics and artificial intelligence. This paper explores the determination of semantic similarity by a number of information sources, which consist of structural semantic information from a lexical taxonomy and information content from a corpus. To investigate how information sources could be used effectively, a variety of strategies for using various possible information sources are implemented. A new measure is then proposed which combines information sources nonlinearly. Experimental evaluation against a benchmark set of human similarity ratings demonstrates that the proposed measure significantly outperforms traditional similarity measures. |
| doi_str_mv | 10.1109/TKDE.2003.1209005 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_922420339</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1209005</ieee_id><sourcerecordid>29419108</sourcerecordid><originalsourceid>FETCH-LOGICAL-c450t-93bead3d8cd6d5b1719d23697f67f6a9c0ea8957a880f6473139b2956d9162773</originalsourceid><addsrcrecordid>eNqFkU9LJDEQxRvZBd1ZP4DsJXhYTz1Wkk4ndRxmdVcUvOjVkOmudiP9Z0y6kfn2m2YGBA8rFFRB_erBq5dlZxyWnANePtz-uloKALnkAhBAHWUnXCmTC478S5qh4HkhC32cfYvxBQCMNvwke1r1zG23YXDVX9YMgXXk4hR8_8wida4ffcWi73zrgh93bEPjG1HP3oZQRzbFmeumdvTblpjvk0DnRj_0LA5TqCh-z742ro10euiL7PH66mH9J7-7_32zXt3lVaFgzFFuyNWyNlVd1mrDNcdayBJ1U6ZyWAE5g0o7Y6ApCy25xI1AVdbIS6G1XGQXe93k5HWiONrOx4ra1vU0TNEicA2IZZnIn_8lBRbpY2A-B02hUOGseP4BfEnm-2TXohCFACkxQXwPVWGIMVBjt8F3LuwsBzsnaOcE7ZygPSSYbn7sbzwRvfOH7T_Ulpcs</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>922420339</pqid></control><display><type>article</type><title>An approach for measuring semantic similarity between words using multiple information sources</title><source>IEEE Electronic Library (IEL)</source><creator>Li, Y. ; Bandar, Z.A. ; Mclean, D.</creator><creatorcontrib>Li, Y. ; Bandar, Z.A. ; Mclean, D.</creatorcontrib><description>Semantic similarity between words is becoming a generic problem for many applications of computational linguistics and artificial intelligence. This paper explores the determination of semantic similarity by a number of information sources, which consist of structural semantic information from a lexical taxonomy and information content from a corpus. To investigate how information sources could be used effectively, a variety of strategies for using various possible information sources are implemented. A new measure is then proposed which combines information sources nonlinearly. Experimental evaluation against a benchmark set of human similarity ratings demonstrates that the proposed measure significantly outperforms traditional similarity measures.</description><identifier>ISSN: 1041-4347</identifier><identifier>EISSN: 1558-2191</identifier><identifier>DOI: 10.1109/TKDE.2003.1209005</identifier><identifier>CODEN: ITKEEH</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial intelligence ; Computational linguistics ; Expert systems ; Human ; Humans ; Image retrieval ; Information sources ; Joining processes ; Linguistics ; Natural language processing ; Semantics ; Similarity ; Statistics ; Strategy ; Taxonomy</subject><ispartof>IEEE transactions on knowledge and data engineering, 2003-07, Vol.15 (4), p.871-882</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2003</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c450t-93bead3d8cd6d5b1719d23697f67f6a9c0ea8957a880f6473139b2956d9162773</citedby><cites>FETCH-LOGICAL-c450t-93bead3d8cd6d5b1719d23697f67f6a9c0ea8957a880f6473139b2956d9162773</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1209005$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1209005$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Li, Y.</creatorcontrib><creatorcontrib>Bandar, Z.A.</creatorcontrib><creatorcontrib>Mclean, D.</creatorcontrib><title>An approach for measuring semantic similarity between words using multiple information sources</title><title>IEEE transactions on knowledge and data engineering</title><addtitle>TKDE</addtitle><description>Semantic similarity between words is becoming a generic problem for many applications of computational linguistics and artificial intelligence. This paper explores the determination of semantic similarity by a number of information sources, which consist of structural semantic information from a lexical taxonomy and information content from a corpus. To investigate how information sources could be used effectively, a variety of strategies for using various possible information sources are implemented. A new measure is then proposed which combines information sources nonlinearly. Experimental evaluation against a benchmark set of human similarity ratings demonstrates that the proposed measure significantly outperforms traditional similarity measures.</description><subject>Artificial intelligence</subject><subject>Computational linguistics</subject><subject>Expert systems</subject><subject>Human</subject><subject>Humans</subject><subject>Image retrieval</subject><subject>Information sources</subject><subject>Joining processes</subject><subject>Linguistics</subject><subject>Natural language processing</subject><subject>Semantics</subject><subject>Similarity</subject><subject>Statistics</subject><subject>Strategy</subject><subject>Taxonomy</subject><issn>1041-4347</issn><issn>1558-2191</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqFkU9LJDEQxRvZBd1ZP4DsJXhYTz1Wkk4ndRxmdVcUvOjVkOmudiP9Z0y6kfn2m2YGBA8rFFRB_erBq5dlZxyWnANePtz-uloKALnkAhBAHWUnXCmTC478S5qh4HkhC32cfYvxBQCMNvwke1r1zG23YXDVX9YMgXXk4hR8_8wida4ffcWi73zrgh93bEPjG1HP3oZQRzbFmeumdvTblpjvk0DnRj_0LA5TqCh-z742ro10euiL7PH66mH9J7-7_32zXt3lVaFgzFFuyNWyNlVd1mrDNcdayBJ1U6ZyWAE5g0o7Y6ApCy25xI1AVdbIS6G1XGQXe93k5HWiONrOx4ra1vU0TNEicA2IZZnIn_8lBRbpY2A-B02hUOGseP4BfEnm-2TXohCFACkxQXwPVWGIMVBjt8F3LuwsBzsnaOcE7ZygPSSYbn7sbzwRvfOH7T_Ulpcs</recordid><startdate>20030701</startdate><enddate>20030701</enddate><creator>Li, Y.</creator><creator>Bandar, Z.A.</creator><creator>Mclean, D.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7TB</scope><scope>FR3</scope><scope>F28</scope></search><sort><creationdate>20030701</creationdate><title>An approach for measuring semantic similarity between words using multiple information sources</title><author>Li, Y. ; Bandar, Z.A. ; Mclean, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c450t-93bead3d8cd6d5b1719d23697f67f6a9c0ea8957a880f6473139b2956d9162773</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Artificial intelligence</topic><topic>Computational linguistics</topic><topic>Expert systems</topic><topic>Human</topic><topic>Humans</topic><topic>Image retrieval</topic><topic>Information sources</topic><topic>Joining processes</topic><topic>Linguistics</topic><topic>Natural language processing</topic><topic>Semantics</topic><topic>Similarity</topic><topic>Statistics</topic><topic>Strategy</topic><topic>Taxonomy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Y.</creatorcontrib><creatorcontrib>Bandar, Z.A.</creatorcontrib><creatorcontrib>Mclean, D.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Engineering Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on knowledge and data engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li, Y.</au><au>Bandar, Z.A.</au><au>Mclean, D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An approach for measuring semantic similarity between words using multiple information sources</atitle><jtitle>IEEE transactions on knowledge and data engineering</jtitle><stitle>TKDE</stitle><date>2003-07-01</date><risdate>2003</risdate><volume>15</volume><issue>4</issue><spage>871</spage><epage>882</epage><pages>871-882</pages><issn>1041-4347</issn><eissn>1558-2191</eissn><coden>ITKEEH</coden><abstract>Semantic similarity between words is becoming a generic problem for many applications of computational linguistics and artificial intelligence. This paper explores the determination of semantic similarity by a number of information sources, which consist of structural semantic information from a lexical taxonomy and information content from a corpus. To investigate how information sources could be used effectively, a variety of strategies for using various possible information sources are implemented. A new measure is then proposed which combines information sources nonlinearly. Experimental evaluation against a benchmark set of human similarity ratings demonstrates that the proposed measure significantly outperforms traditional similarity measures.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TKDE.2003.1209005</doi><tpages>12</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1041-4347 |
| ispartof | IEEE transactions on knowledge and data engineering, 2003-07, Vol.15 (4), p.871-882 |
| issn | 1041-4347 1558-2191 |
| language | eng |
| recordid | cdi_proquest_journals_922420339 |
| source | IEEE Electronic Library (IEL) |
| subjects | Artificial intelligence Computational linguistics Expert systems Human Humans Image retrieval Information sources Joining processes Linguistics Natural language processing Semantics Similarity Statistics Strategy Taxonomy |
| title | An approach for measuring semantic similarity between words using multiple information sources |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T09%3A17%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20approach%20for%20measuring%20semantic%20similarity%20between%20words%20using%20multiple%20information%20sources&rft.jtitle=IEEE%20transactions%20on%20knowledge%20and%20data%20engineering&rft.au=Li,%20Y.&rft.date=2003-07-01&rft.volume=15&rft.issue=4&rft.spage=871&rft.epage=882&rft.pages=871-882&rft.issn=1041-4347&rft.eissn=1558-2191&rft.coden=ITKEEH&rft_id=info:doi/10.1109/TKDE.2003.1209005&rft_dat=%3Cproquest_RIE%3E29419108%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=922420339&rft_id=info:pmid/&rft_ieee_id=1209005&rfr_iscdi=true |