The past decade has seen a steady growth of interest in statistical language models for information retrieval, and much research work has been conducted on this subject. This book by ChengXiang Zhai summarizes most of this research. It opens with an introduction covering the basic concepts of information retrieval and statistical language models, presenting the intuitions behind these concepts. This introduction is then followed by a chapter providing an overview of: r classical information retrieval models, from similarity-based models to probabilistic relevance and inference models, and r two retrieval frameworks, the axiomatic and decision-theoretic retrieval frameworks, both co-developed by the author. This overview covers the main aspects of these models and frameworks, and allows the author to introduce notions that help position the statistical language model to be presented in the following chapters. The remainder of the book is then devoted to the presentation of the statistical language models used in information retrieval, and their application to special tasks. Chapter 3 presents the standard, simple query likelihood retrieval model. After reviewing the basic idea behind this model (at the core of current statistical language models for information retrieval), the author presents the different event models that have been considered: Multinomial, Multiple Bernoulli, and Multiple Poisson. He then explains the strategy for parameter estimation and the different smoothing techniques one can rely on. This presentation is followed by a discussion of the relation between smoothing and tf-idf weighting, which paves the way for the two-stage smoothing method presented in the following section. This chapter is very well written and presents, in a clear yet complete way, the fundamentals of the query likelihood retrieval model. The following chapter, entitled Complex Query Likelihood Retrieval Model, is devoted to extensions of the simple query likelihood model of Chapter 3. In particular, the author reviews document-specific smoothing methods, based on document clustering and document expansion, the use of n-gram models and Markov random fields, as well as the full Bayesian query likelihood and the translation models. The intent here is not to provide a detailed description of these elements, but rather to give an overview, and pointers to extensions and models related to the query likelihood retrieval model.
Relevance feedback has been incorporated into information retrieval models, but there has been difficulty incorporating relevance feedback in a principled way into language modeling (LM) using a query likelihood for information retrieval REF .
Statistical Language Models for Information Retrieval
Statistical Language Models for Information Retrieval
Computer Science