TY - JOUR
AU - Croft, W. Bruce
AB -  LDA-Based Document Models for Ad-hoc Retrieval Xing Wei and W. Bruce Croft Computer Science Department University of Massachusetts Amherst 140 Governors Drive Amherst, MA 01003 {xwei,croft}@cs.umass.edu 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. 1971). The well-known Latent Semantic Indexing (LSI) technique was introduced in 1990 (Deerwester et al, 1990). More recently, Hoffman (1999) described the probabilistic Latent Semantic Indexing (pLSI) technique. This approach uses a latent variable model 
TI - LDA-based document models for ad-hoc retrieval
DO - 10.1145/1148170.1148204
DA - 2006-08-06
UR - https://www.deepdyve.com/lp/association-for-computing-machinery/lda-based-document-models-for-ad-hoc-retrieval-FVTiv6Jjr0
DP - DeepDyve
ER -