Syntactic-Based Methods for Measuring Word Similarity
This paper explores different strategies for extracting similarity relations between words from partially parsed text corpora. The strategies we have analysed do not require supervised training nor semanticinformation available from general lexical resources. They differ in the amount and the qualit...
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| creator | Gamallo, Pablo Gasperin, Caroline Agustini, Alexandre Lopes, Gabriel P. |
| description | This paper explores different strategies for extracting similarity relations between words from partially parsed text corpora. The strategies we have analysed do not require supervised training nor semanticinformation available from general lexical resources. They differ in the amount and the quality of the syntactic contexts against whichwords are compared. The paper presents in details the notion of syntactic context and how syntactic information could be used to extract semantic regularities of word sequences. Finally, experimental tests with Portuguese corpus demonstrate that similarity measures based on fine-grained and elaborate syntactic contexts perform better than those based on poorly defined contexts. |
| doi_str_mv | 10.1007/3-540-44805-5_15 |
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The strategies we have analysed do not require supervised training nor semanticinformation available from general lexical resources. They differ in the amount and the quality of the syntactic contexts against whichwords are compared. The paper presents in details the notion of syntactic context and how syntactic information could be used to extract semantic regularities of word sequences. Finally, experimental tests with Portuguese corpus demonstrate that similarity measures based on fine-grained and elaborate syntactic contexts perform better than those based on poorly defined contexts.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540425571</identifier><identifier>ISBN: 3540425578</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540448055</identifier><identifier>EISBN: 9783540448051</identifier><identifier>DOI: 10.1007/3-540-44805-5_15</identifier><identifier>OCLC: 958522998</identifier><identifier>LCCallNum: QA76.9.N38</identifier><language>eng</language><publisher>Germany: Springer Berlin / Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Exact sciences and technology ; Local Weight ; Noun Phrase ; Related Word ; Speech and sound recognition and synthesis. Linguistics ; Syntactic Information ; Word Similarity</subject><ispartof>Lecture notes in computer science, 2001, Vol.2166, p.116-125</ispartof><rights>Springer-Verlag Berlin Heidelberg 2001</rights><rights>2001 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><relation>Lecture Notes in Computer Science</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttps://ebookcentral.proquest.com/covers/3071696-l.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/3-540-44805-5_15$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/3-540-44805-5_15$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,779,780,784,789,790,793,4050,4051,27925,38255,41442,42511</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=1020390$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Tauser, Karel</contributor><contributor>Mautner, Paul</contributor><contributor>Matousek, Vaclav</contributor><contributor>Moucek, Roman</contributor><contributor>Mautner, Pavel</contributor><contributor>Matoušek, Václav</contributor><contributor>Mouček, Roman</contributor><contributor>Taušer, Karel</contributor><creatorcontrib>Gamallo, Pablo</creatorcontrib><creatorcontrib>Gasperin, Caroline</creatorcontrib><creatorcontrib>Agustini, Alexandre</creatorcontrib><creatorcontrib>Lopes, Gabriel P.</creatorcontrib><title>Syntactic-Based Methods for Measuring Word Similarity</title><title>Lecture notes in computer science</title><description>This paper explores different strategies for extracting similarity relations between words from partially parsed text corpora. The strategies we have analysed do not require supervised training nor semanticinformation available from general lexical resources. They differ in the amount and the quality of the syntactic contexts against whichwords are compared. The paper presents in details the notion of syntactic context and how syntactic information could be used to extract semantic regularities of word sequences. Finally, experimental tests with Portuguese corpus demonstrate that similarity measures based on fine-grained and elaborate syntactic contexts perform better than those based on poorly defined contexts.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Local Weight</subject><subject>Noun Phrase</subject><subject>Related Word</subject><subject>Speech and sound recognition and synthesis. Linguistics</subject><subject>Syntactic Information</subject><subject>Word Similarity</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540425571</isbn><isbn>3540425578</isbn><isbn>3540448055</isbn><isbn>9783540448051</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2001</creationdate><recordtype>book_chapter</recordtype><recordid>eNotUDtPwzAQNk8RSnfGDqwuPl_8GqHiJRUxFMRoOYlDA2kS7HTovydpueVO30unj5BrYHNgTN0iFSmjaaqZoMKCOCKXOCB7QByTBCQARUzNCZkapfccF0LBKUkYMk6NSvGcJEZowbkx-oJMY_xmwyBXIExCxGrX9C7vq5zeu-iL2avv120RZ2UbhtvFbaiar9lnG4rZqtpUtQtVv7siZ6Wro5_-7wn5eHx4XzzT5dvTy-JuSTvQXFBQZekzpyVmmudMygyzDJTIUo1YAhhgsnTDkxqNRF643BvppHGlFkZzjxNyc8jtXMxdXQbX5FW0Xag2LuwsMM7QsEE2P8hiN37rg83a9icOvB17tGiHZuy-Njv2OBjwPze0v1sfe-tHR-6bPrg6X7uu9yFaZAqkkZaDBa7wD2RWbmw</recordid><startdate>2001</startdate><enddate>2001</enddate><creator>Gamallo, Pablo</creator><creator>Gasperin, Caroline</creator><creator>Agustini, Alexandre</creator><creator>Lopes, Gabriel P.</creator><general>Springer Berlin / Heidelberg</general><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>FFUUA</scope><scope>IQODW</scope></search><sort><creationdate>2001</creationdate><title>Syntactic-Based Methods for Measuring Word Similarity</title><author>Gamallo, Pablo ; Gasperin, Caroline ; Agustini, Alexandre ; Lopes, Gabriel P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p1825-17ffeba863b82c066b3bb175b4833f119106fa974839632dace96a69af85982e3</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Local Weight</topic><topic>Noun Phrase</topic><topic>Related Word</topic><topic>Speech and sound recognition and synthesis. Linguistics</topic><topic>Syntactic Information</topic><topic>Word Similarity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gamallo, Pablo</creatorcontrib><creatorcontrib>Gasperin, Caroline</creatorcontrib><creatorcontrib>Agustini, Alexandre</creatorcontrib><creatorcontrib>Lopes, Gabriel P.</creatorcontrib><collection>ProQuest Ebook Central - Book Chapters - Demo use only</collection><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gamallo, Pablo</au><au>Gasperin, Caroline</au><au>Agustini, Alexandre</au><au>Lopes, Gabriel P.</au><au>Tauser, Karel</au><au>Mautner, Paul</au><au>Matousek, Vaclav</au><au>Moucek, Roman</au><au>Mautner, Pavel</au><au>Matoušek, Václav</au><au>Mouček, Roman</au><au>Taušer, Karel</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Syntactic-Based Methods for Measuring Word Similarity</atitle><btitle>Lecture notes in computer science</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2001</date><risdate>2001</risdate><volume>2166</volume><spage>116</spage><epage>125</epage><pages>116-125</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540425571</isbn><isbn>3540425578</isbn><eisbn>3540448055</eisbn><eisbn>9783540448051</eisbn><abstract>This paper explores different strategies for extracting similarity relations between words from partially parsed text corpora. The strategies we have analysed do not require supervised training nor semanticinformation available from general lexical resources. They differ in the amount and the quality of the syntactic contexts against whichwords are compared. The paper presents in details the notion of syntactic context and how syntactic information could be used to extract semantic regularities of word sequences. Finally, experimental tests with Portuguese corpus demonstrate that similarity measures based on fine-grained and elaborate syntactic contexts perform better than those based on poorly defined contexts.</abstract><cop>Germany</cop><pub>Springer Berlin / Heidelberg</pub><doi>10.1007/3-540-44805-5_15</doi><oclcid>958522998</oclcid><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2001, Vol.2166, p.116-125 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_1020390 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Local Weight Noun Phrase Related Word Speech and sound recognition and synthesis. Linguistics Syntactic Information Word Similarity |
| title | Syntactic-Based Methods for Measuring Word Similarity |
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