NDLI: Assessing multivariate Bernoulli models for information retrieval

Content Provider	ACM Digital Library
Author	Losada, David E. Azzopardi, Leif
Copyright Year	2008
Abstract	Although the seminal proposal to introduce language modeling in information retrieval was based on a multivariate Bernoulli model, the predominant modeling approach is now centered on multinomial models. Language modeling for retrieval based on multivariate Bernoulli distributions is seen inefficient and believed less effective than the multinomial model. In this article, we examine the multivariate Bernoulli model with respect to its successor and examine its role in future retrieval systems. In the context of Bayesian learning, these two modeling approaches are described, contrasted, and compared both theoretically and computationally. We show that the query likelihood following a multivariate Bernoulli distribution introduces interesting retrieval features which may be useful for specific retrieval tasks such as sentence retrieval. Then, we address the efficiency aspect and show that algorithms can be designed to perform retrieval efficiently for multivariate Bernoulli models, before performing an empirical comparison to study the behaviorial aspects of the models. A series of comparisons is then conducted on a number of test collections and retrieval tasks to determine the empirical and practical differences between the different models. Our results indicate that for sentence retrieval the multivariate Bernoulli model can significantly outperform the multinomial model. However, for the other tasks the multinomial model provides consistently better performance (and in most cases significantly so). An analysis of the various retrieval characteristics reveals that the multivariate Bernoulli model tends to promote long documents whose nonquery terms are informative. While this is detrimental to the task of document retrieval (documents tend to contain considerable nonquery content), it is valuable for other tasks such as sentence retrieval, where the retrieved elements are very short and focused.
Starting Page	1
Ending Page	46
Page Count	46
File Format	PDF
ISSN	10468188
e-ISSN	15582868
DOI	10.1145/1361684.1361690
Volume Number	26
Issue Number	3
Journal	ACM Transactions on Information Systems (TOIS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-06-20
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Information retrieval Language models Multinomial Multivariate Bernoulli
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Information Systems Business, Management and Accounting

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