Abstract
In this paper, we complement the term frequency, which is used in many bag-of-words based information retrieval models, with information about the semantic relatedness of query and document terms. Our experiments show that when employed in the standard probabilistic retrieval model BM25, the additional semantic information significantly outperforms the standard term frequency, and also improves the effectiveness when additional query expansion is applied. We further analyze the impact of different lexical semantic resources on the IR effectiveness.
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Müller, C., Gurevych, I. (2010). Semantically Enhanced Term Frequency. In: Gurrin, C., et al. Advances in Information Retrieval. ECIR 2010. Lecture Notes in Computer Science, vol 5993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12275-0_56
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DOI: https://doi.org/10.1007/978-3-642-12275-0_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12274-3
Online ISBN: 978-3-642-12275-0
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