Advances in Open Domain Question Answering (Text, Speech and by Tomek Strzalkowski, Sanda Harabagiu

By Tomek Strzalkowski, Sanda Harabagiu

This new Springer quantity offers a finished and specific examine present techniques to automatic query answering. the extent of presentation is appropriate for novices to the sector in addition to for execs wishing to review this sector and/or to construct functional QA platforms. The booklet can function a "how-to" guide for IT practitioners and procedure builders. it might probably even be used to educate graduate classes in laptop technology, details technological know-how and similar disciplines.

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Extra resources for Advances in Open Domain Question Answering (Text, Speech and Language Technology)

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E. they may contain extraneous information. 16 D. MOLDOVAN, M. PAŞCA AND M. SURDEANU Table 6. Performance over TREC Definition questions. ) respectively. 17 Exact answers (Min). 23 To assess the ability of the QA system to extract minimal answers (a task much harder than the extraction of relevant sentences), we also evaluated the returned answers manually. Each answer was marked as correct if it matched a TREC answer key and it was minimal. The scores are shown in the last two rows of Table 6.

SOME ADVANCED FEATURES OF LCC’S POWERANSWER 11 Table 1. Performance over 500 TREC-2002 questions. 9% (98/317). , 2002). The failures of the prover are due primarily to the lack of linguistic axioms. 3. 1 Motivation There are categories of questions for which even complex system modules, like the logic prover described in the previous section, have difficulties extracting precise answers. Questions whose expected answer type does not fall into a well-defined semantic category constitute a challenge to otherwise successful question answering systems.

World Knowledge Axioms Logic Representation of WordNet Glosses A major problem in QA is that often an answer is expressed in words different from the question keywords. World knowledge is necessary to conceptually link questions and answers. WordNet glosses contain a source of world knowledge. To be useful in automated reasoning, the glosses need to be transformed into logic forms. Taking the same approach as for open text, we have parsed and represented in logic forms all WordNet glosses. For example, the gloss definition of concept sport_NN#1 is an active diversion requiring physical exertion and competition, which yields the logic representation: active_JJ(x1) & diversion_NN(x1) & require_VB(e1,x1,x2) & or_CC(x2,x3,x4) & physical_JJ(x3) & exertion_NN(x3) & competition_NN(x4) Lexical Chains A much improved source of world knowledge is obtained when the gloss words are semantically disambiguated (Moldovan and Novischi, 2002).

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