NDLI: A Hybrid Statistical Language Model Applied to the Domain Specific Information Retrieval

Content Provider	IEEE Xplore Digital Library
Author	Wei Wang Kunhui Lin Changle Zhou
Copyright Year	2008
Description	Author affiliation: Software Sch., Xiamen Univ., Xiamen (Wei Wang; Kunhui Lin; Changle Zhou)
Abstract	The traditional language model takes the multi-topics document corpus as the research target. In order to avoid the interference brought by the multi-topics problem, this paper focuses on the domain specific Information Retrieval (IR). In domain specific IR, different terms are considered to take different contribution degrees to the final query result. So the terms in a document can be divided into different categories according to their contribution degrees. And the statistical information of a term, mainly its probabilities, is computed by different methods and smooth strategies according to its category. This paper proposed an improved hybrid statistical language model used in the Domain Specific IR. This new model has about 9%~10% performance increment in the experimental result. In the end, some challenges and research orientation of the statistical language model research are presented.
Starting Page	3
Ending Page	7
File Size	230129
Page Count	5
File Format	PDF
ISBN	9780769533056
DOI	10.1109/FSKD.2008.240
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Handwriting recognition Space technology Natural languages Speech recognition Interference Information retrieval Probability distribution Statistics Fuzzy systems
Content Type	Text
Resource Type	Article

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