Titlebook: Advances in Open Domain Question Answering; Tomek Strzalkowski,Sanda M. Harabagiu Book 2008 Springer Science+Business Media B.V. 2008 Auto

勉励

Barbara Stallings,Saori N. Katada entropy models, the models required can be modelled using any machine learning approach. To perform question answering, as has been discussed in previous chapters, questions are first analyzed and a prediction is made as to what type of answer the user is expecting. Secondly, a fast search of the t

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烤架

https://doi.org/10.1057/9780230354586n in order to be able to relate questions and answers. We present first, variation at the term level which consists in retrieving questions terms in document sentences even if morphologic, syntactic or semantic variations alter them. Our second subject matter concerns variation at the sentence level

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Susanne Durst,Helio Aisenberg Ferenhofithm efficiently identifies hotspots in a large target corpus where the answer might be located. In the second step, an answer selection algorithm analyzes these hotspots, considering such factors as answer type and candidate redundancy, to extract short answer snippets. This chapter describes both

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Competitive Strategies in Life Sciencesso far, and is currently participating in the ARDA AQUAINT program. Our approach centres around the technique of Predictive Annotation, in which an extended set of named entities is recognized prior to indexing, so that the semantic class labels can be indexed along with text and included in the que

Prostatism

Competitive Strategies in Life Sciences answer generation. Such systems are based on information retrieval (IR) techniques such as passage retrieval in conjunction with shallow IE techniques such as named entity tagging. Sentence-level answers may be sufficient in many applications focused on reducing information overload: instead of a l