Text Retrieval
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Introduction to Information Retrieval
Author: Christopher D. Manning
language: en
Publisher: Cambridge University Press
Release Date: 2008-07-07
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.
Text Retrieval Systems In Information Management
This Book Aims At Helping The Reader Develop A Clear Under- Standing Of Text Retrieval Systems, Including Its Nature And Characteristics; Steps To Be Followed In Developing A Text Retrieval System; Software Packages Available For The Purpose; Guidelines For Choosing An Appropriate Software, And So On. To Make The Text Suitable For All Kinds Of Readers, Chapters And The Basics Of Database Technology, Database Management, And File Structures Appropriate For Text Retrieval Systems Have Been Provided. This Book Also Discusses The Major Features Of Library Management Systems (Lmss), The Software Packages Used For Automating Library House-Keeping Operations.The Trend Is To Developing Systems Which Can Provide The Actual Information Sought By The Use Rather Than Reference To The Information Sources Or Part Of The Text Where The Search Term Appears. Such Systems Apply Expert Systems And Natural Language Processing Techniques, And Are Called Knowledge-Based Systems (Kbss). This Book Describes Features Of These Systems And Mentions Some Of The Applications Of Kbss In Library And Information Activities.
Text Retrieval and Filtering
Author: Robert M. Losee
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Text Retrieval and Filtering: Analytical Models of Performance is the first book that addresses the problem of analytically computing the performance of retrieval and filtering systems. The book describes means by which retrieval may be studied analytically, allowing one to describe current performance, predict future performance, and to understand why systems perform as they do. The focus is on retrieving and filtering natural language text, with material addressing retrieval performance for the simple case of queries with a single term, the more complex case with multiple terms, both with term independence and term dependence, and for the use of grammatical information to improve performance. Unambiguous statements of the conditions under which one method or system will be more effective than another are developed. Text Retrieval and Filtering: Analytical Models of Performance focuses on the performance of systems that retrieve natural language text, considering full sentences as well as phrases and individual words. The last chapter explicitly addresses how grammatical constructs and methods may be studied in the context of retrieval or filtering system performance. The book builds toward solving this problem, although the material in earlier chapters is as useful to those addressing non-linguistic, statistical concerns as it is to linguists. Those interested in grammatical information should be cautioned to carefully examine earlier chapters, especially Chapters 7 and 8, which discuss purely statistical relationships between terms, before moving on to Chapter 10, which explicitly addresses linguistic issues. Text Retrieval and Filtering: Analytical Models of Performance is suitable as a secondary text for a graduate level course on Information Retrieval or Linguistics, and as a reference for researchers and practitioners in industry.