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Building a textual entailment system for the RTE3 competition. Application to a QA system
Abstract Textual entailment recognition (RTE) is thTextual entailment recognition (RTE) is the task of deciding, when given two text fragments, whether the meaning of one text is entailed (can be inferred) from the other text. Last year, we built our first Textual Entailment (TE) system, with which we participated in the RTE31 competition. The main idea of this system is to transform the hypothesis making use of extensive semantic knowledge from sources like DIRT, WordNet, Wikipedia and acronyms database. Additionally, the system applies complex grammar rules for rephrasing in English and uses the results of a module we built to acquire the extra background knowledge needed. In the first part, we presented the system architecture and the results, whose best run ranked 3rd in RTE3 among 45 participating runs of 26 groups. The second part of the paper presents the manner in which we adapted the TE system in order to include it in a Question Answering (QA) system. The aim of using the TE system as a module in the general architecture of a QA system is to improve the ranking between possible answers for questions in which the answer type is Measure, Person, Location, Date and Organization., Person, Location, Date and Organization.
Abstractsub Textual entailment recognition (RTE) is thTextual entailment recognition (RTE) is the task of deciding, when given two text fragments, whether the meaning of one text is entailed (can be inferred) from the other text. Last year, we built our first Textual Entailment (TE) system, with which we participated in the RTE31 competition. The main idea of this system is to transform the hypothesis making use of extensive semantic knowledge from sources like DIRT, WordNet, Wikipedia and acronyms database. Additionally, the system applies complex grammar rules for rephrasing in English and uses the results of a module we built to acquire the extra background knowledge needed. In the first part, we presented the system architecture and the results, whose best run ranked 3rd in RTE3 among 45 participating runs of 26 groups. The second part of the paper presents the manner in which we adapted the TE system in order to include it in a Question Answering (QA) system. The aim of using the TE system as a module in the general architecture of a QA system is to improve the ranking between possible answers for questions in which the answer type is Measure, Person, Location, Date and Organization., Person, Location, Date and Organization.
Bibtextype inproceedings  +
Doi 10.1109/SYNASC.2008.73  +
Has author Adrian Iftene +
Has extra keyword Background knowledge + , Grammar rules + , QA system + , Question answering systems + , Semantic knowledge + , System architectures + , Text fragments + , Textual entailment + , Wikipedia + , Wordnet + , Competition + , Computation theory + , Natural language processing systems + , Character recognition +
Isbn 9780769535234  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 116–122  +
Published in Proceedings of the 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2008 +
Title Building a textual entailment system for the RTE3 competition. Application to a QA system +
Type conference paper  +
Year 2008 +
Creation dateThis property is a special property in this wiki. 7 November 2014 03:30:13  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 03:30:13  +
DateThis property is a special property in this wiki. 2008  +
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Building a textual entailment system for the RTE3 competition. Application to a QA system + Title
 

 

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