Statistical Language Models for Information Retrieval

ChengXiang Zhai is a Professor of Computer Science and Willett Faculty Scholar at the University of Illinois at Urbana-Champaign, where he is also affiliated with the Graduate School of Library and Information Science, Institute for Genomic Biology, and Department of Statistics. He received a Ph.D. in Computer Science from Nanjing University in 1990, and a Ph.D. in Language and Information Technologies from Carnegie Mellon University in 2002. He worked at Clairvoyance Corp. as a Research Scientist and then Senior Research Scientist from 1997 to 2000. His research interests include information retrieval, text mining, natural language processing, machine learning, biomedical and health informatics, and intelligent education information systems. He has published over 200 research papers in major conferences and journals. He is an Associate Editor for Information Processing and Management and previously served as an Associate Editor of ACM Transactions on Information Systems, and on the editorial board of Information Retrieval Journal. He is a conference program co-chair of ACM CIKM 2004, NAACL HLT 2007, ACM SIGIR 2009, ECIR 2014, ICTIR 2015, and WWW 2015, and conference general co-chair for ACM CIKM 2016. He is an ACM Distinguished Scientist and a recipient of multiple awards, including the ACM SIGIR 2004 Best Paper Award, the ACM SIGIR 2014 Test of Time Paper Award, Alfred P. Sloan Research Fellowship, IBM Faculty Award, HP Innovation Research Program Award, Microsoft Beyond Search Research Award, and the Presidential Early Career Award for Scientists and Engineers (PECASE).

As online information grows dramatically, search engines such as Google are playing a more and more important role in our lives. Critical to all search engines is the problem of designing an effective retrieval model that can rank documents accurately for a given query. This has been a central research problem in information retrieval for several decades. In the past ten years, a new generation of retrieval models, often referred to as statistical language models, has been successfully applied to solve many different information retrieval problems. Compared with the traditional models such as the vector space model, these new models have a more sound statistical foundation and can leverage statistical estimation to optimize retrieval parameters. They can also be more easily adapted to model non-traditional and complex retrieval problems. Empirically, they tend to achieve comparable or better performance than a traditional model with less effort on parameter tuning. This book systematically reviews the large body of literature on applying statistical language models to information retrieval with an emphasis on the underlying principles, empirically effective language models, and language models developed for non-traditional retrieval tasks. All the relevant literature has been synthesized to make it easy for a reader to digest the research progress achieved so far and see the frontier of research in this area. The book also offers practitioners an informative introduction to a set of practically useful language models that can effectively solve a variety of retrieval problems. No prior knowledge about information retrieval is required, but some basic knowledge about probability and statistics would be useful for fully digesting all the details. Table of Contents: Introduction / Overview of Information Retrieval Models / Simple Query Likelihood Retrieval Model / Complex Query Likelihood Model / Probabilistic Distance Retrieval Model / Language Models for Special Retrieval Tasks / Language Models for Latent Topic Analysis / Conclusions

Introduction.- Overview of Information Retrieval Models.- Simple Query Likelihood Retrieval Model.- Complex Query Likelihood Model.- Probabilistic Distance Retrieval Model.- Language Models for Special Retrieval Tasks.- Language Models for Latent Topic Analysis.- Conclusions.