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.
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Piao, S., Rea, B., McNaught, J., Ananiadou, S. (2010). Improving Full Text Search with Text Mining Tools. In: Horacek, H., Métais, E., Muñoz, R., Wolska, M. (eds) Natural Language Processing and Information Systems. NLDB 2009. Lecture Notes in Computer Science, vol 5723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12550-8_29
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DOI: https://doi.org/10.1007/978-3-642-12550-8_29
Publisher Name: Springer, Berlin, Heidelberg
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