Question answering using maximum entropy components
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
An analysis of the AskMSR question-answering system
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Building a reusable test collection for question answering
Journal of the American Society for Information Science and Technology - Research Articles
Exploring correlation of dependency relation paths for answer extraction
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
An exploration of the principles underlying redundancy-based factoid question answering
ACM Transactions on Information Systems (TOIS)
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
A probabilistic graphical model for joint answer ranking in question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Information distance from a question to an answer
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Information Theory
Overview of the answer validation exercise 2006
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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In this paper, an information distance based approach is proposed to perform answer validation for question answering system. To validate an answer candidate, the approach calculates the conditional information distance between the question focus and the candidate under certain condition pattern set. Heuristic methods are designed to extract question focus and generate proper condition patterns from question. General search engines are employed to estimate the Kolmogorov complexity, hence the information distance. Experimental results show that our approach is stable and flexible, and outperforms traditional tfidf methods.