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VDM Verlag Enhancing Factoid Question Answering Using Frame Semantics: How FrameNet may Improve Answer Passage Retrieval and Answer Processing Effectiveness in Natural Language Question Answering Systems

Name: Enhancing Factoid Question Answering Using Frame Semantics: How FrameNet may Improve Answer Passage Retrieval and Answer Processing Effectiveness in Natural Language Question Answering Systems
Brand: VDM Verlag
Price: 70.0 GBP
Availability: InStock

Computing & Internet

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As the rapid rise of information stored in document databases continues, there is a real possibility of using these textual databases in systems that automatically provide answers to questions issued by users in natural language. Identification of candidate answers, within these documents, using Question Answering systems, is a challenging task that has been tackled by many researchers. One of the main problems identified in this domain is to retrieve text passages that potentially contain answers to the questions. Another problem is the extraction of text excerpts that are highly likely to answer the questions. This book discusses the work on utilizing Frame Semantics encapsulated in FrameNet to enhance the performance of semantic QA systems. FrameNet provides scenario-based generalizations over lexical items that share similar semantic backgrounds. This work also addresses some other challenges that influence frame semantic-based QA effectiveness. This includes the level of frame semantic parsing of texts, lexical coverage in FrameNet, and methods to fuse a FrameNet-based answer processing model with a non-semantic model.

Product Specifications

Brand: VDM Verlag
Format: paperback
ASIN: 3639292707
Category: Books > Subjects > Computing & Internet
Domain: Amazon UK
Release Date: 22 June 2011
Listed Since: 25 June 2011

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VDM Verlag Enhancing Factoid Question Answering Using Frame Semantics: How FrameNet may Improve Answer Passage Retrieval and Answer Processing Effectiveness in Natural Language Question Answering Systems

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VDM Verlag Enhancing Factoid Question Answering Using Frame Semantics: How FrameNet may Improve Answer Passage Retrieval and Answer Processing Effectiveness in Natural Language Question Answering Systems

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