A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Information retrieval as statistical translation
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
Title language model for information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A formal study of information retrieval heuristics
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Diagnostic Evaluation of Information Retrieval Models
ACM Transactions on Information Systems (TOIS)
Statistical Translation Language Model for Twitter Search
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Exploiting proximity feature in statistical translation models for information retrieval
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Hi-index | 0.00 |
Statistical translation models have been shown to outperform simple document language models which rely on exact matching of words in the query and documents. A main challenge in applying translation models to ad hoc information retrieval is to estimate a translation model without training data. In this paper, we perform axiomatic analysis of translation language model for retrieval in order to gain insights about how to optimize the estimation of translation probabilities. We propose a set of constraints that a reasonable translation language model should satisfy. We check these constraints on the state-of-the-art translation estimation method based on Mutual Information and find that it does not satisfy most of the constraints. We then propose a new estimation method that better satisfies the defined constraints. Experimental results on representative TREC data sets show that the proposed new estimation method outperforms the existing Mutual Information-based estimation, suggesting that the proposed constraints are indeed helpful for designing better estimation methods for translation language model.