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
An exploration of axiomatic approaches to information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical Language Models for Expert Finding in Enterprise Corpora
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
An analysis on document length retrieval trends in language modeling smoothing
Information Retrieval
Language Models for Web Object Retrieval
NISS '09 Proceedings of the 2009 International Conference on New Trends in Information and Service Science
Quality models for microblog retrieval
Proceedings of the 21st ACM international conference on Information and knowledge management
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A standard approach for determining a Dirichlet smoothing parameter is to choose a value which maximizes a retrieval performance metric using training data consisting of queries and relevance judgments. There are, however, situations where training data does not exist or the queries and relevance judgments do not reflect typical user information needs for the application. We propose an unsupervised approach for estimating a Dirichlet smoothing parameter based on collection statistics. We show empirically that this approach can suggest a plausible Dirichlet smoothing parameter value in cases where relevance judgments cannot be used.