Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic model of information retrieval: development and comparative experiments Part 2
Information Processing and Management: an International Journal
Hybrid Pre-Query Term Expansion using Latent Semantic Analysis
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
The VLDB Journal — The International Journal on Very Large Data Bases
Query expansion using a collection dependent probabilistic latent semantic thesaurus
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
The Sensitivity of Latent Dirichlet Allocation for Information Retrieval
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
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Probabilistic latent semantic analysis (PLSA) is a method of calculating term relationships within a document set using term frequencies. It is well known within the information retrieval community that raw term frequencies contain various biases that affect the precision of the retrieval system. Weighting schemes, such as BM25, have been developed in order to remove such biases and hence improve the overall quality of results from the retrieval system. We hypothesised that the biases found within raw term frequencies also affect the calculation of term relationships performed during PLSA. By using portions of the BM25 probabilistic weighting scheme, we have shown that applying weights to the raw term frequencies before performing PLSA leads to a significant increase in precision at 10 documents and average reciprocal rank. When using the BM25 weighted PLSA information in the form of a thesaurus, we achieved an average 8% increase in precision. Our thesaurus method was also compared to pseudo-relevance feedback and a co-occurrence thesaurus, both using BM25 weights. Precision results showed that the probabilistic latent semantic thesaurus using BM25 weights outperformed each method in terms of precision at 10 documents and average reciprocal rank.