A language model assigns a probability to a piece of unseen text, based on some training data. For example, a language model based on a big English newspaper archive is expected to assign a higher probability to “a bit of text” than to “aw pit tov tags”, because the words in the former phrase (or word pairs or word triples if so-called n-gram models are used) occur more frequently in the data than the words in the latter phrase. For information retrieval, typical usage is to build a language model for each document. At search time, the top ranked document is the one which’ language model assigns the highest probability to the query.
The term language models originates from probabilistic models of language generation developed for automatic speech recognition systems in the early 1980’s. Speech recognition systems use a language model to complement the results of the acoustic model which models the relation between words (or parts of words called phonemes) and the acoustic signal. The history of language models, however, goes back to beginning of the 20th century when Andrei Markov used language models (Markov models) to model letter sequences in works of Russian literature. Another famous application of language models are Claude Shannon’s models of letter sequences and word sequences, which he used to illustrate the implications of coding and information theory. In the 1990’s language models were applied as a general tool for several natural language processing applications, such as part-of-speech tagging, machine translation, and optical character recognition. Language models were applied to information retrieval by a number of research groups in the late 1990’s. They became rapidly popular in information retrieval research. By 2001, the ACM SIGIR conference had two separate sessions on language models containing 5 papers in total. In 2003, a group of leading information retrieval researchers published a research road map “Challenges in Information Retrieval and Language Modeling”, indicating that the future of information retrieval and the future of language modeling can not be seen apart from each other.
This entry will soon be published in the Encyclopedia of Database Systems by Springer. The Encyclopedia, under the editorial guidance of Ling Liu and M. Tamer Özsu, will be a multiple volume, comprehensive, and authoritative reference on databases, data management, and database systems. Since it will be available in both print and online formats, researchers, students, and practitioners will benefit from advanced search functionality and convenient interlinking possibilities with related online content. The Encyclopedia's online version will be accessible on the SpringerLink platform. Click here for more information about the Encyclopedia of Database Systems.
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We organize the 9th Dutch-Belgian Information Retrieval Workshop (DIR) in Twente. The event will take place on February 2-3, 2009 in our lovely “Vrijhof” building. The primary aim of Dutch-Belgian Information Retrieval (DIR) workshop is to provide an international meeting place where researchers from the domain of information retrieval and related disciplines, can exchange information and present innovative research developments. The first call for papers is just out. Papers may range from theoretical work to system descriptions. We encourage Ph.D. students to submit their research. We also welcome contributions from industry when they focus on novel research directions. Have a look at the DIR website at: 
An expert finding system allows a user to type a simple text query and retrieve names and contact information of individuals that possess the expertise expressed in the query. This paper proposes a novel approach to expert finding in large enterprises or intranets by modeling candidate experts (persons), web documents and various relations among them with so-called expertise graphs. As distinct from the state-of-the-art approaches estimating personal expertise through one-step propagation of relevance probability from documents to the related candidates, our methods are based on the principle of multi-step relevance propagation in topic-specific expertise graphs. We model the process of expert finding by probabilistic random walks of three kinds: finite, infinite and absorbing. Experiments on TREC Enterprise Track data originating from two large organizations show that our methods using multi-step relevance propagation improve over the baseline one-step propagation based method in almost all cases. 
We will start a new NWO research project on the use of keyword auctions for distributed information retrieval. The project's aim is to distribute internet search functionality in such a way that communities of users and/or federations of small search systems provide search services in a collaborative way. Instead of getting all data to a centralized point and process queries centrally, as is done by today's search systems, the project will distribute queries over many small autonomous search systems and process them locally. Distributed information retrieval is a well researched sub area of information retrieval, but it has not resulted in practical solutions for large scale search problems because of high administration costs of setting up large numbers of installations and because it turns out to be hard in practice to direct queries to the appropriate local search systems. In this project we will research a radical new approach to distribute search: distributed information retrieval by means of keyword auctions.