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A “Bag” or a “Window” of Words for Information Filtering?

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Artificial Intelligence: Theories, Models and Applications (SETN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5138))

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Abstract

Treating documents as bag of words is the norm in Information Filtering. Syntactic and semantic correlations between terms are ignored, or in other words, term independence is assumed. In this paper we challenge this common assumption. We use Nootropia, a user profiling model that uses a sliding window approach to capture term dependencies in a network and a spreading activation process to take them into account for document evaluation. Experiments performed based on TREC’s routing guidelines demonstrate that given an adequate window size the additional information that term dependencies encode, results in improved filtering performance over a traditional bag of words approach.

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Authors and Affiliations

  1. Centre for Research and Technology - Thessaly (CE.RE.TE.TH), Greece

    Nikolaos Nanas & Manolis Vavalis

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  1. Nikolaos Nanas
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  2. Manolis Vavalis
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John Darzentas George A. Vouros Spyros Vosinakis Argyris Arnellos

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Nanas, N., Vavalis, M. (2008). A “Bag” or a “Window” of Words for Information Filtering?. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2008. Lecture Notes in Computer Science(), vol 5138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87881-0_17

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  • DOI: https://doi.org/10.1007/978-3-540-87881-0_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87880-3

  • Online ISBN: 978-3-540-87881-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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