A Comparative Study of Utilizing Topic Models for Information Retrieval
Publication Date
2009
Journal or Book Title
ADVANCES IN INFORMATION RETRIEVAL, PROCEEDINGS
Abstract
We explore the utility of different types of topic models for retrieval purposes. Based on prior work, we describe several ways that topic models can be integrated into the retrieval process. We evaluate the effectiveness of different types of topic models within those retrieval approaches. We show that: (1) topic models are effective for document smoothing; (2) more rigorous topic models such as Latent Dirichlet Allocation provide gains over cluster-based models; (3) more elaborate topic models that capture topic dependencies provide no additional gains; (4) smoothing documents by using their similar documents is as effective as smoothing them by using topic models; (5) doing query expansion should utilize topics discovered in the top feedback documents instead of coarse-grained topics from the whole corpus; (6) generally, incorporating topics in the feedback documents for building relevance models can benefit the performance more for queries that have more relevant documents.
DOI
https://doi.org/10.1007/978-3-642-00958-7_6
Pages
29-41
Volume
5478
Book Series Title
LECTURE NOTES IN COMPUTER SCIENCE
Recommended Citation
Yi, X and Allan, J, "A Comparative Study of Utilizing Topic Models for Information Retrieval" (2009). ADVANCES IN INFORMATION RETRIEVAL, PROCEEDINGS. 284.
https://doi.org/10.1007/978-3-642-00958-7_6