PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On Text-Based Estimation of Document Relevance
Eerika Savia, Samuel Kaski, Ville Tuulos and Petri Myllymäki
In: IJCNN 2004, 25 - 29 July 2004, Budapest, Hungary.


This work is part of a proactive information retrieval project that aims at estimating relevance from implicit user feedback. The noisy feedback signal needs to be complemented with all available information, and textual content is one of the natural sources. Here we take the first steps by investigating whether this source is at all useful in the challenging setting of estimating the relevance of a new document based on only few samples with known relevance. It turns out that even sophisticated unsupervised methods like multinomial PCA (or Latent Dirichlet Allocation) cannot help much. By contrast, feature extraction supervised by relevant auxiliary data may help.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:83
Deposited By:Eerika Savia
Deposited On:14 May 2004