Statistical Language Models for Information Retrieval / by Chengxiang Zhai.

Format:

Book

Author/Creator:

Zhai, ChengXiang, Author.

Series:

Synthesis Lectures on Human Language Technologies, 1947-4059

Language:

English

Subjects (All):

Artificial intelligence.

Natural language processing (Computer science).

Computational linguistics.

Artificial Intelligence.

Natural Language Processing (NLP).

Computational Linguistics.

Local Subjects:

Artificial Intelligence.

Natural Language Processing (NLP).

Computational Linguistics.

Physical Description:

1 online resource (XII, 132 p.)

Edition:

1st ed. 2009.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2009.

Summary:

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.

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.

ISBN:

9783031021305

3031021304