Automated reasoning: 33 BASIC research problems
Automated reasoning: 33 BASIC research problems
Artificial Intelligence - Special volume on natural language processing
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Lexical chains for question answering
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Logic form transformation of WordNet and its applicability to question answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
COGEX: a logic prover for question answering
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
Models for the semantic classification of noun phrases
CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
An inference model for semantic entailment in natural language
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Method for extracting commonsense knowledge
Proceedings of the fifth international conference on Knowledge capture
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This paper describes the system that LCC has devised to perform textual entailment recognition for the PASCAL RTE Challenge. Our system transforms each text-hypothesis pair into a two-layered logic form representation that expresses the lexical, syntactic, and semantic attributes of the text and hypothesis. A large set of natural language axioms are constructed for each text-hypothesis pair that help connect concepts in the hypothesis with concepts in the text. Our natural language logic prover is then used to prove entailment through abductive reasoning. The system's performance in the challenge resulted in an accuracy of 55%.