Artificial Intelligence - Special volume on natural language processing
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Discovery of inference rules for question-answering
Natural Language Engineering
Logical Forms in the core language engine
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Exploiting paraphrases in a Question Answering system
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Capturing and answering questions posed to a knowledge-based system
Proceedings of the 4th international conference on Knowledge capture
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
A question-answering system for high school algebra word problems
AFIPS '64 (Fall, part I) Proceedings of the October 27-29, 1964, fall joint computer conference, part I
Representations of knowledge in a program for solving physics problems
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Building an end-to-end text reading system based on a packed representation
FAM-LbR '10 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading
Improving the quality of text understanding by delaying ambiguity resolution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Improving the quality of text understanding by delaying ambiguity resolution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Creating correct, semantic representations of questions is essential for applications that can use formal reasoning to answer them. However, even within a restricted domain, it is hard to anticipate all the possible ways that a question might be phrased, and engineer reliable processing modules to produce a correct semantic interpretation for the reasoner. In our work on posing questions to a biology knowledge base, we address this brittleness in two ways: First, we exploit the DIRT paraphrase database to introduce alternative phrasings of a question; Second, we defer word sense and semantic role commitment until question answering. Resulting ambiguities are then resolved by interleaving additional interpretation with question-answering, allowing the combinatorics of alternatives to be controlled and domain knowledge to guide paraphrase and sense selection. Our evaluation suggests that the resulting system is able to understand exam-style questions more reliably.