A Neural Network for Text Representation

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.

• Type: Conference
• Year: 2005
• Where: ICANN
• Authors: Mikaela Keller, Samy Bengio
• Keywords: Classical Vector Space, ICANN 2005, Neural Network, Retrieval Tasks