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Return to Session Va: Language models
This paper presents a new dependence language modeling approach to information retrieval. The approach extends the basic language modeling approach based on unigram by relaxing the independence assumption. We integrate the linkage of a query as a hidden variable, which expresses the term dependencies within the query as an acyclic, planar, undirected graph. We then assume that a query is generated from a document in two stages: the linkage is generated first, and then each term is generated in turn depending on other related terms according to the linkage. We also present a smoothing method for model parameter estimation and an approach to learning the linkage of a sentence in an unsupervised manner. The new approach is compared to the classical probabilistic retrieval model and the previously proposed language models with and without taking into account term dependencies. Results show that our model achieves substantial and significant improvements on TREC collections.
@inproceedings{1009024, author = {Jianfeng Gao and Jian-Yun Nie and Guangyuan Wu and Guihong Cao}, title = {Dependence language model for information retrieval}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {170--177}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009024}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
