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.