Document Retrieval Index

Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.

Examines Concepts, Functions & Processes of Information Retrieval Systems

This compilation of original papers on information retrieval presents an overview, covering both general theory and specific methods, of the development and current status of information retrieval systems. Each chapter contains several papers carefully chosen to represent substantive research work that has been carried out in that area, each is preceded by an introductory overview and followed by supported references for further reading.

"The primary purpose of an information retrieval system is to retrieve all the relevant documents, which are relevant to the user query. The Latent Semantic Indexing (LSI) based ad hoc document retrieval task investigates the performance of retrieval systems that search a static set of documents using new questions/queries. Performance of LSI has been tested for several smaller datasets (e.g., MED, CISI abstracts etc) however, LSI has not been tested for a large dataset. In this research, we concentrated on the performance of LSI on large dataset. Stop word list and term weighting schemes are two key parameters in the area of information retrieval. We investigated the performance of LSI by using three different set of stop word lists and, also, without removing the stop words from the test collection. We also applied three different term-weighting (raw term frequency, log-entropy, and tf-idf) schemes to measure retrieval performance of LSI. We observed that, firstly, for a LSI based ad hoc information retrieval system, a tailored stop word list must be assembled for every unique large dataset. Secondly, the use of tf-idf term weighting scheme shows better retrieval performance than log-entropy and raw term frequency weighting schemes even when the test collection became large."--P. ii.

Interested in how an efficient search engine works? Want to know what algorithms are used to rank resulting documents in response to user requests? The authors answer these and other key information retrieval design and implementation questions. This book is not yet another high level text. Instead, algorithms are thoroughly described, making this book ideally suited for both computer science students and practitioners who work on search-related applications. As stated in the foreword, this book provides a current, broad, and detailed overview of the field and is the only one that does so. Examples are used throughout to illustrate the algorithms. The authors explain how a query is ranked against a document collection using either a single or a combination of retrieval strategies, and how an assortment of utilities are integrated into the query processing scheme to improve these rankings. Methods for building and compressing text indexes, querying and retrieving documents in multiple languages, and using parallel or distributed processing to expedite the search are likewise described. This edition is a major expansion of the one published in 1998. Besides updating the entire book with current techniques, it includes new sections on language models, cross-language information retrieval, peer-to-peer processing, XML search, mediators, and duplicate document detection.