Large test collection experiments on an operational, interactive system: Okapi at TREC
TREC-2 Proceedings of the second conference on Text retrieval conference
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Bridging the lexical chasm: statistical approaches to answer-finding
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Manual and automatic evaluation of summaries
AS '02 Proceedings of the ACL-02 Workshop on Automatic Summarization - Volume 4
Automatically evaluating answers to definition questions
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Phrase-based definitional question answering using definition terminology
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Answering Definition Question: Ranking for Top-k
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Informative sentence retrieval for domain specific terminologies
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Using AOBP for definitional question answering
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Can click patterns across user's query logs predict answers to definition questions?
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Contextual Language Models For Ranking Answers To Natural Language Definition Questions
Computational Intelligence
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This paper proposes a probabilistic model for definitional question answering (QA) that reflects the characteristics of the definitional question. The intention of the definitional question is to request the definition about the question target. Therefore, an answer for the definitional question should contain the content relevant to the topic of the target, and have a representation form of the definition style. Modeling the problem of definitional QA from both the topic and definition viewpoints, the proposed probabilistic model converts the task of answering the definitional questions into that of estimating the three language models: topic language model, definition language model, and general language model. The proposed model systematically combines several evidences in a probabilistic framework. Experimental results show that a definitional QA system based on the proposed probabilistic model is comparable to state-of-the-art systems.