Name
Abstract | ||
|---|---|---|
Search algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied since the 60s and has recently produced good results in the language model framework. An approach to building topic models based on a formal generative model of documents, Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use LDA to improve ad-hoc retrieval. We propose an LDA-based document model within the language modeling framework, and evaluate it on several TREC collections. Gibbs sampling is employed to conduct approximate inference in LDA and the computational complexity is analyzed. We show that improvements over retrieval using cluster-based models can be obtained with reasonable efficiency. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1148170.1148204 | SIGIR |
Keywords | Field | DocType |
language modeling framework,information retrieval,cluster-based model,building topic model,lda-based document model,ad-hoc retrieval,language model framework,formal generative model,topic model,cluster-based retrieval,gibbs sampling,computational complexity,machine learning,search algorithm,latent dirichlet allocation,language model | Dynamic topic model,Data mining,Divergence-from-randomness model,Latent Dirichlet allocation,Computer science,Document clustering,Pachinko allocation,Artificial intelligence,Language model,Information retrieval,Topic model,Machine learning,Generative model | Conference |
ISBN | Citations | PageRank |
1-59593-369-7 | 526 | 23.24 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xing Wei | 1 | 1141 | 60.87 |
| W. Bruce Croft | 2 | 17812 | 2796.94 |