Abstract
Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. This is a fundamental problem in natural language understanding that provides a broad framework for studying language variability and has a large number of applications. This paper presents a principled approach to this problem that builds on inducing representations of text snippets into a hierarchical knowledge representation along with a sound optimization-based inferential mechanism that makes use of it to decide semantic entailment. A preliminary evaluation on the PASCAL text collection is presented.
Original language | English (US) |
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Pages | 1043-1049 |
Number of pages | 7 |
State | Published - 2005 |
Event | 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05 - Pittsburgh, PA, United States Duration: Jul 9 2005 → Jul 13 2005 |
Other
Other | 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05 |
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Country/Territory | United States |
City | Pittsburgh, PA |
Period | 7/9/05 → 7/13/05 |
ASJC Scopus subject areas
- Software
- Artificial Intelligence