Research on Question Answering & Evaluation: A Survey

Question Answering (QA) is the next generation of search engine which is related to natural language processing, information retrieval and etc. QA aims at providing more powerful information access tools to help users overcome the problem of information overloading. In the last decade, QA has become an important subfield of NLP and IR. Its development track, i.e. accelerating research via systematical and large scale evaluation, and some successful experiences, such as the effectiveness of partial-parsing techniques based on character surface and the importance of fast NLP tools, have made it a great and most important impetus to the research of NLP. Moreover, QA has built a more effective connection between NLP research and NLP application. It will be helpful to review the history and investigate state of the art of QA.

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[21]  Grace Hui Yang,et al.  The Integration of Lexical Knowledge and External Resources for Question Answering , 2002, TREC.

[22]  Jimmy J. Lin,et al.  Data-Intensive Question Answering , 2001, TREC.

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[25]  Ellen M. Voorhees,et al.  Overview of the TREC-9 Question Answering Track , 2000, TREC.

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