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- W1571050549 abstract "Although the management of information assets-specifically, of text documents that make up 80 percent of these assets-an provide organizations with a competitive advantage, the ability of information retrieval (IR) systems to deliver relevant information to users is severely hampered by the difficulty of disambiguating natural language. The word ambiguity problem is addressed with moderate success in restricted settings, but continues to be the main challenge for general settings, characterized by large, heterogeneous document collections.In this paper, we provide preliminary evidence for the usefulness of statistical natural language processing (NLP) techniques, and specifically of collocation indexing, for IR in general settings. We investigate the effect of three key parameters on collocation indexing performance: directionality, distance, and weighting. We build on previous work in IR to (1) advance our knowledge of key design elements for collocation indexing, (2) demonstrate gains in retrieval precision from the use of statistical NLP for general-settings IR, and, finally, (3) provide practitioners with a useful cost-benefit analysis of the methods under investigation." @default.
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- W1571050549 date "2007-01-01" @default.
- W1571050549 modified "2023-09-25" @default.
- W1571050549 title "Enhancing Information Retrieval through Statistical Natural Language Processign: A Study of Collocation Indexing" @default.
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- W1571050549 doi "https://doi.org/10.2307/25148806" @default.
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