PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A neural network for text representation
Mikaela Keller and Samy Bengio
In: ICANN 2005, 11-15 Sept 2005, Warsaw.


Text categorization and retrieval tasks are often based on a good representation of textual data. Departing from the classical vector space model, several probabilistic models have been proposed recently, such as PLSA. In this paper, we propose the use of a neural network based, non-probabilistic, solution, which captures jointly a rich representation of words and documents. Experiments performed on two information retrieval tasks using the TDT2 database and the TREC-8 and 9 sets of queries yielded a better performance for the proposed neural network model, as compared to PLSA and the classical TFIDF representations.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:1046
Deposited By:Mikaela Keller
Deposited On:12 August 2005