A neural network for text representation

Authors:
Mikaela Keller;Samy Bengio
Affiliations:
IDIAP Research Institute, Martigny, Switzerland;IDIAP Research Institute, Martigny, Switzerland
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

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.