Using Language Models For Information Retrieval

Abstract

Because of the world wide web, information retrieval systems are now used by millions of untrained users all over the world. The search engines that perform the information retrieval tasks, often retrieve thousands of potentially interesting documents to a query. The documents should be ranked in decreasing order of relevance in order to be useful to the user. This book describes a mathematical model of information retrieval based on the use of statistical language models. The approach uses simple document-based unigram models to compute for each document the probability that it generates the query. This probability is used to rank the documents. The study makes the following research contributions. The development of a model that integrates term weighting, relevance feedback and structured queries. The development of a model that supports multiple representations of a request or information need by integrating a statistical translation model. The development of a model that supports multiple representations of a document, for instance by allowing proximity searches or searches for terms from a particular record field (e.g. a search for terms from the title). A mathematical interpretation of stop word removal and stemming. A mathematical interpretation of operators for mandatory terms, wildcards and synonyms. A practical comparison of a language model-based retrieval system with similar systems that are based on well-established models and term weighting algorithms in a controlled experiment. The application of the model to cross-language information retrieval and adaptive information filtering, and the evaluation of two prototype systems in a controlled experiment. Experimental results on three standard tasks show that the language model-based algorithms work as well as, or better than, today's top-performing retrieval algorithms. The standard tasks investigated are ad-hoc retrieval (when there are no previously retrieved documents to guide the search), retrospective relevance weighting (find the optimum model for a given set of relevant documents), and ad-hoc retrieval using manually formulated Boolean queries. The application to cross-language retrieval and adaptive filtering shows the practical use of respectively structured queries, and relevance feedback.