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Improving full text search with text mining tools

Research output: Contribution in Book/Report/ProceedingsChapter

Published

Publication date2010
Host publicationNatural language processing and information systems: 14th International Conference on Applications of Natural Language to Information Systems, NLDB 2009, Saarbrücken, Germany, June 24-26, 2009. Revised Papers
EditorsHelmut Horacek, Elisabeth Métais, Rafael Muñoz, Magdalena Wolska
Place of publicationBerlin
PublisherSpringer
Pages301-302
Number of pages2
ISBN (Electronic)9783642125508
ISBN (Print)9783642125492
Original languageEnglish

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume5723
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Abstract

Today, academic researchers face a flood of information. Full text search provides an important way of finding useful information from mountains of publications, but it generally suffers from low precision, or low quality of document retrieval. A full text search algorithm typically examines every word in a given text, trying to find the query words. Unfortunately, many words in natural language are polysemous, and thus many documents retrieved using this approach are irrelevant to actual search queries.