NDLI: Pruning dependency trees in a syntactic information retrieval model

Content Provider	IEEE Xplore Digital Library
Author	Chang Liu Hui Wang McClean, S. Jun Liu Shengli Wu
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. & Math, Univ. of Ulster, Newtownabbey (Chang Liu)
Abstract	Natural language processing (NLP) techniques are believe to have the potential to aid information retrieval (IR) in terms of retrieval accuracy. In previous work, we report a proof of concept study on a new approach to NLP-based IR, proposed as a syntactic IR model (SIR). In SIR, Documents and queries are represented on the basis of syntactic parse trees, which are generated by a natural language parser. Based on this tree structured representation of documents and queries, the matching between a document and a query is executed on their tree representations, with tree comparison as the key operation. In this paper, we extend the dataset of the IR experiment for testing SIR into full documents. Additionally, the raw parse trees output by the parser are pruned before being fed into the indexing process of SIR. This operation is necessary and has the similar role of the stop words list strategy in the term based IR index construction. Experimental results show that retrieval accuracy is improved if the pruning operation is applied.
Starting Page	285
Ending Page	291
File Size	209428
Page Count	7
File Format	PDF
ISBN	9781424417230
DOI	10.1109/ICALIP.2008.4590035
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Economics Pediatrics Natural languages Speech Information retrieval Construction industry Indexing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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