Effectively Using Syntax for Recognizing False Entailment
Recognizing textual entailment is a challenging problem and a fundamental component of many applications in natural language processing. We present a novel framework for recognizing textual entailment that focuses on the use of syntactic heuristics to recognize false entailment. We give a thorough a...
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| creator | Snow, Rion Vanderwende, Lucy Menezes, Arul |
| description | Recognizing textual entailment is a challenging problem and a fundamental component of many applications in natural language processing. We present a novel framework for recognizing textual entailment that focuses on the use of syntactic heuristics to recognize false entailment. We give a thorough analysis of our system, which demonstrates state-of-the-art performance on a widely-used test set.
Published in the Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, p33-40, Jun 2006. |
| format | Report |
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Published in the Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, p33-40, Jun 2006.</description><subject>FALSE ENTAILMENT</subject><subject>HEURISTIC METHODS</subject><subject>INFORMATION PROCESSING</subject><subject>Information Science</subject><subject>LEXICAL SIMILARITY</subject><subject>Linguistics</subject><subject>NATURAL LANGUAGE</subject><subject>Operations Research</subject><subject>PARAPHRASE DETECTION</subject><subject>PASCAL RTE TEST SET</subject><subject>PERFORMANCE(ENGINEERING)</subject><subject>STATE OF THE ART</subject><subject>SYNTAX</subject><fulltext>true</fulltext><rsrctype>report</rsrctype><creationdate>2006</creationdate><recordtype>report</recordtype><sourceid>1RU</sourceid><recordid>eNrjZLB0TUtLTS7JLEvNqVQILc7MS1cIrswrSaxQSMsvUghKTc5Pz8usAgm7JeYUpyq4AuUyc3JT80p4GFjTQEK8UJqbQcbNNcTZQzelJDM5vrgkMy-1JN7RxdHE1MzM0tSYgDQAcngr5w</recordid><startdate>200606</startdate><enddate>200606</enddate><creator>Snow, Rion</creator><creator>Vanderwende, Lucy</creator><creator>Menezes, Arul</creator><scope>1RU</scope><scope>BHM</scope></search><sort><creationdate>200606</creationdate><title>Effectively Using Syntax for Recognizing False Entailment</title><author>Snow, Rion ; Vanderwende, Lucy ; Menezes, Arul</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-dtic_stinet_ADA4566953</frbrgroupid><rsrctype>reports</rsrctype><prefilter>reports</prefilter><language>eng</language><creationdate>2006</creationdate><topic>FALSE ENTAILMENT</topic><topic>HEURISTIC METHODS</topic><topic>INFORMATION PROCESSING</topic><topic>Information Science</topic><topic>LEXICAL SIMILARITY</topic><topic>Linguistics</topic><topic>NATURAL LANGUAGE</topic><topic>Operations Research</topic><topic>PARAPHRASE DETECTION</topic><topic>PASCAL RTE TEST SET</topic><topic>PERFORMANCE(ENGINEERING)</topic><topic>STATE OF THE ART</topic><topic>SYNTAX</topic><toplevel>online_resources</toplevel><creatorcontrib>Snow, Rion</creatorcontrib><creatorcontrib>Vanderwende, Lucy</creatorcontrib><creatorcontrib>Menezes, Arul</creatorcontrib><creatorcontrib>STANFORD UNIV CA DEPT OF COMPUTER SCIENCE</creatorcontrib><collection>DTIC Technical Reports</collection><collection>DTIC STINET</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Snow, Rion</au><au>Vanderwende, Lucy</au><au>Menezes, Arul</au><aucorp>STANFORD UNIV CA DEPT OF COMPUTER SCIENCE</aucorp><format>book</format><genre>unknown</genre><ristype>RPRT</ristype><btitle>Effectively Using Syntax for Recognizing False Entailment</btitle><date>2006-06</date><risdate>2006</risdate><abstract>Recognizing textual entailment is a challenging problem and a fundamental component of many applications in natural language processing. We present a novel framework for recognizing textual entailment that focuses on the use of syntactic heuristics to recognize false entailment. We give a thorough analysis of our system, which demonstrates state-of-the-art performance on a widely-used test set.
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| language | eng |
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| source | DTIC Technical Reports |
| subjects | FALSE ENTAILMENT HEURISTIC METHODS INFORMATION PROCESSING Information Science LEXICAL SIMILARITY Linguistics NATURAL LANGUAGE Operations Research PARAPHRASE DETECTION PASCAL RTE TEST SET PERFORMANCE(ENGINEERING) STATE OF THE ART SYNTAX |
| title | Effectively Using Syntax for Recognizing False Entailment |
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