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High performance question/answering
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Scaling question answering to the web
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Mining the web for answers to natural language questions
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Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
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Trainable question-answering systems
Accurate methods for the statistics of surprise and coincidence
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The mathematics of statistical machine translation: parameter estimation
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Learning to find answers to questions on the Web
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BLEU: a method for automatic evaluation of machine translation
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ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
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HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
ACM Transactions on Asian Language Information Processing (TALIP)
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
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SLPAT '10 Proceedings of the NAACL HLT 2010 Workshop on Speech and Language Processing for Assistive Technologies
PTM: probabilistic topic mapping model for mining parallel document collections
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Query rewriting using monolingual statistical machine translation
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CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Improved answer ranking in social question-answering portals
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Frontiers of Computer Science in China
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EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In this paper we describe and evaluate a Question Answering (QA) system that goes beyond answering factoid questions. Our approach to QA assumes no restrictions on the type of questions that are handled, and no assumption that the answers to be provided are factoids. We present an unsupervised approach for collecting question and answer pairs from FAQ pages, which we use to collect a corpus of 1 million question/answer pairs from FAQ pages available on the Web. This corpus is used to train various statistical models employed by our QA system: a statistical chunker used to transform a natural language-posed question into a phrase-based query to be submitted for exact match to an off-the-shelf search engine; an answer/question translation model, used to assess the likelihood that a proposed answer is indeed an answer to the posed question; and an answer language model, used to assess the likelihood that a proposed answer is a well-formed answer. We evaluate our QA system in a modular fashion, by comparing the performance of baseline algorithms against our proposed algorithms for various modules in our QA system. The evaluation shows that our system achieves reasonable performance in terms of answer accuracy for a large variety of complex, non-factoid questions.