NDLI: Question-Answering of UGC

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Question-Answering of UGC

Content Provider	Scilit
Copyright Year	2014
Description	Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 9.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Building machines that are capable of answering natural language questions is one of the biggest challenges in natural language processing and has a history dating back to the late 1950s [634]. A renewed interest in question-answering started in 1999 with a series of NIST-sponsored TREC Question-Answering (QA) Tracks, large-scale evaluations of domainindependent question-answering1 that produced a few high performance question-answering systems capable of answering factoid and list questions [525, 478, 283]. In 2011, IBM's open-domain question-answering system, Watson [215], beat the two highest ranked Jeopardy!2 players, marking a significant milestone in the more than 60-year quest to create a viable questionanswering machine. Book Name: Mining User Generated Content
Related Links	https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/b16413-21&type=chapterpdf
Ending Page	298
Page Count	34
Starting Page	265
DOI	10.1201/b16413-21
Language	English
Publisher	Informa UK Limited
Publisher Date	2014-01-28
Access Restriction	Open
Subject Keyword	Book Name: Mining User Generated Content Question Answering Building Viable Machine Natural Language Answering System
Content Type	Text
Resource Type	Chapter

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