Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/14606
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- Title
- Recognizing textual entailment via atomic propositions
- Related
- Machine learning challenges : evaluating predictive uncertainty, visual object classification and recognizing textual entailment : First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005 : revised selected papers, p.385-403
- Related
- Lecture notes in computer science : lecture notes in artificial intelligence 3944
- Publisher
- Berlin, Germany : Springer Verlag
- Date
- 2006
- Author/Creator
- Akhmatova, Elena
- Author/Creator
- Mollá, Diego
- Description
- This paper describes Macquarie University’s Centre for Language Technology contribution to the PASCAL 2005 Recognizing Textual Entailment challenge. Our main aim was to test the practicability of a purely logical approach. For this, atomic propositions were extracted from both the text and the entailment hypothesis and they were expressed in a custom logical notation. The text entails the hypothesis if every proposition of the hypothesis is entailed by some proposition in the text. To extract the propositions and encode them into a logical notation the system uses the output of Link Parser. To detect the independent entailment relations the system relies on the use of Otter and WordNet.
- Description
- 19 page(s)
- Subject Keyword
- machine learning
- Subject Keyword
- computer science
- Resource Type
- book chapter
- Organisation
- Macquarie University. Dept. of Computing
- Identifier
- http://hdl.handle.net/1959.14/14606
- Identifier
- ISBN:3540334270
- Identifier
- mq-rm-2006003885
- Language
- eng
