Language modeling for information retrieval

Cover of: Language modeling for information retrieval |

Published by Kluwer Academic Publishers in Dordrecht, Boston .

Written in English

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Subjects:

  • Information storage and retrieval systems,
  • Computational linguistics,
  • Linguistic models

Edition Notes

Book details

Statementedited by W. Bruce Croft and John Lafferty
SeriesKluwer international series on information retrieval -- 13
ContributionsCroft, W. Bruce, Lafferty, John
Classifications
LC ClassificationsZ699.3 .L36 2003
The Physical Object
Paginationxiii, 245 p. :
Number of Pages245
ID Numbers
Open LibraryOL17095105M
ISBN 101402012160
LC Control Number2003047402

Download Language modeling for information retrieval

Language Modeling for Information Retrieval (The Information Retrieval Series) rd Edition by W. Bruce Croft (Editor), John Lafferty (Editor) ISBN About this book.

About this book. A statisticallanguage model, or more simply a language model, is a prob­ abilistic mechanism for generating text. Such adefinition is general enough to include an endless variety of schemes.

However, a distinction should be made between generative models, which can in principle be used to synthesize artificial text, and discriminative techniques to classify text into. A statisticallanguage model, or more simply a language model, is a prob­ abilistic mechanism for generating text.

Such adefinition is general enough to include an endless variety of schemes. However, a distinction should be made between generative models, which can in principle be used to synthesize artificial text, and discriminative.

Language Modeling for Information Retrieval John Lafferty, ChengXiang Zhai (auth.), W. Bruce Croft, John Lafferty (eds.) A statisticallanguage model, or more simply a language model, is a prob­ abilistic mechanism for generating text.

Such adefinition is general enough to include an endless variety of schemes. Browse Books. Home Browse by Title Books Language Modeling for Information Retrieval. Language Modeling for Information Retrieval June June Read More. Authors: W. Bruce Croft, John Lafferty; Publisher: Language Modeling for Information Retrieval.

Abstract. Language models for information retrieval. A common suggestion to users for coming up with goodqueries is to think of words that would likely appear in a relevantdocument, and to use those words as the query.

The language modelingapproach to IR directly models that idea: a document is a good match toa query if the document model is likely to generate the query, whichwill in turn happen if. SIGIR ' Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval A language modeling approach to information retrieval Pages – Information Retrieval Vol.

2, No. 3 () – c C. Zhai DOI: / Statistical Language Models for Information Retrieval A Critical Review ChengXiang Zhai University of Illinois at Urbana-Champaign, N. Goodwin, Urbana, ILUSA, [email protected] Abstract Statistical language models have recently been.

Introduction to Information Retrieval. This is the companion website for the following book. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press.

You can order this book at CUP, at your local bookstore or on the best search term to use is the ISBN: Major Information Retrieval Models. The following major models have been developed to retrieve information: the Boolean model, the Statistical model, which includes the vector space and the probabilistic retrieval model, and the Linguistic and Knowledge-based models.

The first model is often referred to as the "exact match" model; the latter ones as the "best match" models [Belkin. 12 Language models for information retrieval Language models The query likelihood model Language modeling versus other approaches in information retrieval Extended language modeling approaches References and further reading 13 Text classiÞcation and Naive Bayes The text classiÞcation.

• (Sparck Jones et al., ) A probabilistic model of information retrieval: development and comparative experiments. • (Ponte and Croft, ) A language modeling approach to information retrieval • (Zhai and Lafferty, ) A study of smoothing methods for language models applied to ad hoc information retrieval.

In general, statistical language models provide a principled way of modeling various kinds of retrieval problems. Statistical Language Models for Information Retrieval reviews the development of this language modeling approach. It surveys a wide range of retrieval models based on language modeling and attempts to make connections between this Author: ChengXiang Zhai.

The book can be used as a supplementary textbook for a graduate or undergraduate course on information retrieval or related topics (e.g., natural language processing, machine learning) to help students gain in-depth understanding of the basic language models for information retrieval.

Probabilistic relevance models based on document and query generation -- 2. Relevance models in information retrieval -- 3. Language modeling and relevance -- 4. Contributions of language modeling to the theory and practice of IR -- 5. Language models for topic tracking -- 6. A probabilistic approach to term translation for cross-lingual.

A Language Modeling Approach to Information Retrieval Jay M. Ponte and W. Bruce Croft Computer Science Department University of Massachusetts, Amherst {ponte, croft}& Abstract Models of document indexing and docu- ment retrieval have been extensively studied.

The in. Language modeling approaches are used in a variety of other language technologies, such as speech recognition and machine translation, and the book shows that applications such as Web search, cross-lingual search, filtering, and summarization can be described in the same formal framework.

Song and W. Croft. A general language model for information retrieval. In Proceedings of Eighth International Conference on Information and Knowledge Management (CIKM ) 6.

Chen and J. Goodman. An empirical study of smoothing techniques for language modeling. In Proceedings of the 34th Annual Meeting of the ACL.

LM vs. BIM vs. XML retrieval ()Language models and the most successful XML retrieval models approach relevance modeling in a roundabout way as apposed to the BIM model that evaluates relevance initially appears to not include relevance modeling The most successful XML retrieval models assume that queries and.

Statistical Language Models for Information Retrieval book. Read reviews from world’s largest community for readers. As online information grows dramatic /5(3). 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. Language models are used in information retrieval in the query likelihood model.

There, a separate language model is associated with each document in a collection. Documents are ranked based on the probability of the query Q in the document's language model {\displaystyle M_ {d}}.

[Show full abstract] models benefits from language specific preprocessing in terms of retrieval quality.

Also Language Modeling approach is the best performing retrieval model when language. The retrieval/scoring algorithm is subject to heuristics/ constraints, and it varies from one IR model to another. For example, a term frequency constraint specifies that a document with more occurrences of a query term should be scored higher than a document with fewer occurrences of the query term.

Also, the retrieval algorithm may be provided with additional information in the form of. Abstract. This paper presents an analysis of what language modeling (LM) is in the context of information retrieval (IR).

We argue that there are two principal contributions of the language modeling approach. First, that it brings the thinking, theory, and practical knowledge of research in related fields to bear on the retrieval problem.

Statistical language models have recently been successfully applied to many information retrieval problems. A great deal of recent work has shown that statistical language models not only lead to superior empirical performance, but also facilitate parameter tuning and open up possibilities for modeling non-traditional retrieval problems.

Those areas are retrieval models, cross-lingual retrieval, Web search, user modeling, filtering, topic detection and tracking, classification, summarization, question answering, metasearch, distributed retrieval, multimedia retrieval, information extraction, as well as testbed requirements for future work.

Language models are largely used in document retrieval search for book recommendation [16, 20]. Metzler and Croft's Markov Random Field (MRF) model [21, 22] integrates multiword phrases in the query. Specifically, we used the Sequential Dependence Model (SDM), which is a special case of MRF.

Information retrieval (IR) is the activity of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that.

implementing a language modeling framework for information retrieval a dissertation submitted to the department of computer science and the committee on graduate studies of the indian statistical institute in partial fulfillment of the requirements for the degree of master of technology, computer science debasis ganguly july   Abstract.

Statistical language models have recently been successfully applied to many information retrieval problems. A great deal of recent work has shown that statistical language models not only lead to superior empirical performance, but also facilitate parameter tuning and open up possibilities for modeling nontraditional retrieval problems.

The idea of the language modeling approach to information retrieval is to estimate the language model for a document and then to compute the likelihood that the query would have been generated from the estimated model.

Given a query q and a document d, we are interested in estimating the. Information Retrieval: Algorithms and Heuristics is a comprehensive introduction to the study of information retrieval covering both effectiveness and run-time performance. The focus of the presentation is on algorithms and heuristics used to find documents relevant to the user request and to find them fast.

Through multiple examples, the most commonly used algorithms and heuristics needed. About this Item: Oxford Higher Education/Oxford University Press, Softcover.

Condition: New. Natural Language Processing and Information Retrieval is a textbook designed to meet the requirements of engineering students pursuing undergraduate and postgraduate programs in computer science and information technology.

main challenge of constructing a reasonable retrieval model is to flnd a smoothed language model for p(¢jd). Smoothing in Poisson Retrieval Model In general, we want to assign non-zero rates for the query terms that are not seen in document d. Many smoothing methods have been proposed for multinomial language mod-els[2, 28, 29].

Applied to information retrieval, language modeling refers to the problem of estimating the likelihood that a query and a document could have been generated by the same language model, given the.

Summary: This book contains the first collection of papers addressing recent developments in the design of information retrieval systems using language modeling techniques.

Language modeling approaches are used in a variety of other language technologies, such as speech recognition and machine translation, and the book shows. The group’s mission is to advance the state-of-the-art on deep learning and its application to natural language and image understanding, and for making progress on conversational models and methods.

Our research interests are: Neural language modeling for natural language. (optional) Language model smoothing: Zhai and Lafferty (TOIS ) If you are interested in learning more about language modeling for IR, the book "Statistical Language Models for Information Retrieval" by ChengXiang Zhai is recommended.

9: Enhanced Language Modeling (local smoothing and proximity-based models) Tue. 10/9 [WBC] Ch [CDM] Ch Definition of “Information Retrieval” 2 Task description 2 Dealing with uncertainty 3 Research questions 6 Thesis overview 7 PART I.

Background 9 Chapter 2. Information Retrieval Models 11 From library science to IR 11 Properties of indexing languages 12 Introduction into automatic indexing 15. Language modeling for information retrieval Query-likelihood Retrieval Model Smoothing Pseudo-relevance feedback and priors Thursday, Febru 4 What is a language model?

“The goal of a language model is to assign a probability to a sequence of words by means of a probability.Natural Language Processing and Information Retrieval is a textbook designed to meet the requirements of engineering students pursuing undergraduate and postgraduate programs in computer science and information technology.

The book attempts to bridge the gap between theory and practice and would also serve as a useful reference for professionals and researchers working on/5(19).

The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.” Handbook of Natural Language Processing: “The Second Edition presents practical tools and techniques for implementing natural language processing in computer systems.

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