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LDA-based document models for ad-hoc retrieval
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.
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Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Cited by
ArnetMiner: extraction and mining of academic social networks
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1402008 · Cited by 2,231
(2009) · Cited by 1,900
topicmodels : An R Package for Fitting Topic Models
Grün, B., & Hornik, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v040.i13 · Cited by 1,072
Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding
Xiong, C., Power, R., & Callan, J. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052558 · Cited by 301
Chen, Z., & Liu, B. (2018) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00737ed1v01y201610aim033 · Cited by 154
Metadata
- Authors
- Wei, X., & Croft, W. B.
- Venue
- Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2006-08-06
- Pages
- 178-185
- OpenAlex
- W2042980227
- Cited by
- 1,075
- References
- 1
- FWCI
- 32.83
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wei, X., & Croft, W. B. (2006). LDA-based document models for ad-hoc retrieval. Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1148170.1148204
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
