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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Iftene, A. |
| Copyright Year | 2008 |
| Description | Author affiliation: Fac. of Comput. Sci., Al. I. Cuza Univ. Iasi, Iasi, Romania (Iftene, A.) |
| Abstract | Textual 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 RTE3 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 $3^{rd}$ 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. |
| Starting Page | 116 |
| Ending Page | 122 |
| File Size | 190306 |
| Page Count | 7 |
| File Format | |
| ISBN | 9780769535234 |
| DOI | 10.1109/SYNASC.2008.73 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-09-26 |
| Publisher Place | Romania |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Scientific computing Answer Validation Exercise Humans Textual Entailment Question Answering Wikipedia Application software Tellurium Computer science Text recognition Databases System performance Sections |
| Content Type | Text |
| Resource Type | Article |
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