Topic based language models for ad hoc information retrieval

• Published in: 2004 IEEE International Joint Conference on Neural Networks Vol. 4; pp. 3281 - 3286 vol.4
• Main Authors: Azzopardi, L., Girolami, M., van Rijsbergen, C.J.
• Format: Conference Proceeding
• Language: English
• Published: Piscataway NJ IEEE 2004
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Hidden Markov models; Information retrieval; Information systems. Data bases; Mathematical model; Memory organisation. Data processing; Optical computing; Smoothing methods; Software; Vocabulary; Information retrieval; Modelling language; Neural network; Probabilistic approach; Database query; Modeling
• ISBN: 0780383591; 9780780383593
• ISSN: 1098-7576
• DOI: 10.1109/IJCNN.2004.1381205

We propose a topic based approach to language modelling for ad-hoc information retrieval (IR). Many smoothed estimators used for the multinomial query model in IR rely upon the estimated background collection probabilities. In this paper, we propose a topic based language modelling approach, that uses a more informative prior based on the topical content of a document. In our experiments, the proposed model provides comparable IR performance to the standard models, but when combined in a two stage language model, it outperforms all other estimated models.