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ICTIR
2009
Springer

Semi-parametric and Non-parametric Term Weighting for Information Retrieval

13 years 10 months ago
Semi-parametric and Non-parametric Term Weighting for Information Retrieval
Abstract. Most of the previous research on term weighting for information retrieval has focused on developing specialized parametric term weighting functions. Examples include TF.IDF vector-space formulations, BM25, and language modeling weighting. Each of these term weighting functions takes on a specific parametric form. While these weighting functions have proven to be highly effective, they impose strict constraints on the functional form of the term weights. Such constraints may possibly degrade retrieval effectiveness. In this paper we propose two new classes of term weighting schemes that we call semi-parametric and nonparametric weighting. These weighting schemes make fewer assumptions about the underlying term weights and allow the data to speak for itself. We argue that these robust weighting schemes have the potential to be significantly more effective compared to existing parametric schemes, especially with the growing amount of training data becoming available.
Donald Metzler, Hugo Zaragoza
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ICTIR
Authors Donald Metzler, Hugo Zaragoza
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