Thesaurus-Based Automatic Indexing

Thesaurus-Based Automatic Indexing

Luis M. de Campos (University of Granada, Spain)
Copyright: © 2009 |Pages: 15
DOI: 10.4018/978-1-59904-990-8.ch020
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Abstract

In this chapter, we present a thesaurus application in the field of text mining and more specifically automatic indexing on the set of descriptors defined by a thesaurus. We begin by presenting various definitions and a mathematical thesaurus model, and also describe various examples of real world thesauri which are used in official institutions. We then explore the problem of thesaurus-based automatic indexing by describing its difficulties and distinguishing features and reviewing previous work in this area. Finally, we propose various lines of future research.
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Introduction

Automated text categorization (Sebastiani, 2002) is a successful subfield of information management and has to date been the subject of more than six hundred research publications during more than forty-five years of work (Sebastiani & Gabrilovich, 2007). Although it is intrinsically a research field (many scientists and engineers attempt to build good text classifiers using different methods) as part of text mining, this discipline can also be helpful for obtaining knowledge such as document summarization, concept or entity extraction, sentiment analysis, or document clustering from documents. Even in the field of information retrieval (van Rijsbergen, 1979) it is interesting for documents to be previously categorized into a list of classes so that they may be retrieved more accurately using associated keywords as meta-information.

Text documents and natural language both share the common problems of lexical ambiguity or polysemy (i.e. words or expressions having more than one meaning and which is a special case of homonymy) and synonymy (different terms or expressions for the same concept). Additionally, the most common representation used for a text document is the bag of words approach (a model similar to “first-order word approximation” used by Shannon in 1948) and this reduces a document to a list of unrelated terms usually resulting in loss of contextual meaning and structure of certain expressions present in the text.

One tool which has been intrinsically designed to avoid ambiguities of any class is a thesaurus. This is a set of terms with orthogonal meanings and a set of the hierarchical relationships between them. A thesaurus can be very useful in different areas of text mining (Berry, 2003) by removing ambiguity and identifying a document’s context. In this chapter, we present some thesaurus basics, a formal characterization, and several examples of real world thesauri. We will then focus on the problem of automatic indexing on a domain of categories defined on a thesaurus.

Key Terms in this Chapter

Document Indexing: Is the act of describing a document by index terms to indicate, with that metadata, what the document is about or to summarize its content. The index terms are often selected from some form of controlled vocabulary, e.g. a thesaurus.

Text Categorization: Is the task of assigning an electronic document to one or more categories, based on its contents. If only one category is assigned, we refer to that as single-label categorization. If several categories can be assigned to the document, we are dealing with multi-label categorization.

Vector Space Model: Is an algebraic model for representing documents (not only text) as vectors of identifiers, such as, for example, index terms. It is used in information filtering, information retrieval, indexing and relevancy rankings. Its first use was in the SMART Information Retrieval System.

Descriptor: A word or a list of words that represents a concept without ambiguity. It can be used to retrieve documents in an information system, for instance a catalog or a search engine.

Thesaurus: A thesaurus is a list of every important descriptors in a given domain of knowledge; and, for each descriptor, the set of descriptor related with it.

Unsupervised Classification: Is a machine learning technique where a model is fit to observations. In this case there is no a priori known output (as in supervised classification). In unsupervised classification, a data set of input objects is partitioned into different groups or clusters, so that the objects in each group share some common trait, e.g. proximity according to some defined distance measure.

Supervised Classification: Is a machine learning technique for creating a function from training data. The training data consist of pairs of input objects (typically vectors), and desired outputs (categories). The task of the supervised learner is to predict the value of the function for any valid input object after having seen a number of training examples (i.e. pairs of input and target output). To achieve this, the learner has to generalize from the presented data to unseen situations in a “reasonable” way.

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Editorial Advisory Board
Table of Contents
Foreword
Xiaohua Hu
Preface
Min Song, Yi-Fang Brook Wu
Acknowledgment
Min Song, Yi-Fang Brook Wu
Chapter 1
Ying Liu
In the automated text classification, a bag-of-words representation followed by the tfidf weighting is the most popular approach to convert the... Sample PDF
On Document Representation and Term Weights in Text Classification
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Chapter 2
Yi-fang Brook Wu, Quanzhi Li
Document keyphrases provide semantic metadata which can characterize documents and produce an overview of the content of a document. This chapter... Sample PDF
Deriving Document Keyphrases for Text Mining
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Chapter 3
John Atkinson
This chapter introduces a novel evolutionary model for intelligent text mining. The model deals with issues concerning shallow text representation... Sample PDF
Intelligent Text Mining: Putting Evolutionary Methods and Language Technologies Together
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Chapter 4
Xiaoyan Yu, Manas Tungare, Weigo Yuan, Yubo Yuan, Manuel Pérez-Quiñones, Edward A. Fox
Syllabi are important educational resources. Gathering syllabi that are freely available and creating useful services on top of the collection... Sample PDF
Automatic Syllabus Classification Using Support Vector Machines
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Chapter 5
Xiao-Li Li
In traditional text categorization, a classifier is built using labeled training documents from a set of predefined classes. This chapter studies a... Sample PDF
Partially Supervised Text Categorization
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Chapter 6
Yu-Jin Zhang
Mining techniques can play an important role in automatic image classification and content-based retrieval. A novel method for image classification... Sample PDF
Image Classification and Retrieval with Mining Technologies
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Chapter 7
Han-joon Kim
This chapter introduces two practical techniques for improving Naïve Bayes text classifiers that are widely used for text classification. The Naïve... Sample PDF
Improving Techniques for Naïve Bayes Text Classifiers
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Chapter 8
Ricco Rakotomalala, Faouzi Mhamdi
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Using the Text Categorization Framework for Protein Classification
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Chapter 9
Wilson Wong
Feature-based semantic measurements have played a dominant role in conventional data clustering algorithms for many existing applications. However... Sample PDF
Featureless Data Clustering
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Chapter 10
Xiaohui Cui
In this chapter, we introduce three nature inspired swarm intelligence clustering approaches for document clustering analysis. The major challenge... Sample PDF
Swarm Intelligence in Text Document Clustering
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Chapter 11
P. Viswanth
Clustering is a process of finding natural grouping present in a dataset. Various clustering methods are proposed to work with various types of... Sample PDF
Some Efficient and Fast Approaches to Document Clustering
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Chapter 12
Abdelmalek Amine, Zakaria Elberrichi, Michel Simonet, Ladjel Bellatreche, Mimoun Malki
The classification of textual documents has been the subject of many studies. Technologies like the Web and numerical libraries facilitated the... Sample PDF
SOM-Based Clustering of Textual Documents Using WordNet
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Chapter 13
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With the rapid increase of the huge amount of online information, there is a strong demand for Web text mining which helps people discover some... Sample PDF
A Multi-Agent Neural Network System for Web Text Mining
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Chapter 14
Sangeetha Kutty
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Frequent Mining on XML Documents
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Chapter 15
Richi Nayak
XML has gained popularity for information representation, exchange and retrieval. As XML material becomes more abundant, its heterogeneity and... Sample PDF
The Process and Application of XML Data Mining
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Chapter 16
Francesco Buccafurri
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Approximate Range Querying over Sliding Windows
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Chapter 17
Jan H. Kroeze
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Slicing and Dicing a Linguistic Data Cube
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Chapter 18
Yi-fang Brook Wu, Xin Chen
This chapter presents a methodology for personalized knowledge discovery from text. Traditionally, problems with text mining are numerous rules... Sample PDF
Discovering Personalized Novel Knowledge from Text
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Chapter 19
Catia Pesquita
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Untangling BioOntologies for Mining Biomedical Information
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Chapter 20
Luis M. de Campos
In this chapter, we present a thesaurus application in the field of text mining and more specifically automatic indexing on the set of descriptors... Sample PDF
Thesaurus-Based Automatic Indexing
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Chapter 21
Concept-Based Text Mining  (pages 346-358)
Stanley Loh, Leandro Krug Wives, Daniel Lichtnow, José Palazzo M. de Oliveira
The goal of this chapter is to present an approach to mine texts through the analysis of higher level characteristics (called “concepts’)... Sample PDF
Concept-Based Text Mining
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Chapter 22
Marcello Pecoraro
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Statistical Methods for User Profiling in Web Usage Mining
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Chapter 23
Quanzhi Li, Yi-fang Brook Wu
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Chapter 24
Pawan Lingras
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Hyperlink Structure Inspired by Web Usage
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Chapter 25
Rosa Meo, Maristella Matera
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Designing and Mining Web Applications: A Conceptual Modeling Approach
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Chapter 26
Brigitte Trousse, Marie-Aude Aufaure, Bénédicte Le Grand, Yves Lechevallier, Florent Masseglia
This chapter proposes an original approach for ontology management in the context of Web-based information systems. Our approach relies on the usage... Sample PDF
Web Usage Mining for Ontology Management
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Chapter 27
Yue-Shi Lee
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Chapter 28
Stanley R.M. Oliveira, Osmar R. Zaïane
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Privacy-Preserving Data Mining on the Web: Foundations and Techniques
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Chapter 29
G.S. Mahalakshmi, S. Sendhilkumar
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Automatic Reference Tracking
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Chapter 30
Wilson Wong
As more electronic text is readily available, and more applications become knowledge intensive and ontology-enabled, term extraction, also known as... Sample PDF
Determination of Unithood and Termhood for Term Recognition
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Chapter 31
Fotis Lazarinis
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Retrieving Non-Latin Information in a Latin Web: The Case of Greek
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Chapter 32
Anne Kao
Latent Semantic Analysis (LSA) or Latent Semantic Indexing (LSI), when applied to information retrieval, has been a major analysis approach in text... Sample PDF
Latent Semantic Analysis and Beyond
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Chapter 33
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Chapter 34
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Chapter 35
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Chapter 36
Nitin Agarwal, Huan Liu, Jianping Zhang
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A Study of Friendship Networks and Blogosphere
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Chapter 37
Pasquale De Meo
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Chapter 38
Diego Liberati
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Chapter 39
Shuting Xu
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Current Issues and Future Analysis in Text Mining for Information Security Applications
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Chapter 40
E. Thirumaran
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Collaborative Filtering Based Recommendation Systems
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Chapter 41
Hanna Suominen
The purpose of this chapter is to provide an overview of prevalent measures for evaluating the quality of system output in seven key text mining... Sample PDF
Performance Evaluation Measures for Text Mining
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Chapter 42
Yanliang Qi
The biology literatures have been increased in an exponential growth in recent year. The researchers need an effective tool to help them find out... Sample PDF
Text Mining in Bioinformatics: Research and Application
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Chapter 43
Ki Jung Lee
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Chapter 44
Richard S. Segall
This chapter presents background on text mining, and comparisons and summaries of seven selected software for text mining. The text mining software... Sample PDF
A Survey of Selected Software Technologies for Text Mining
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Chapter 45
Ah Chung Tsoi, Phuong Kim To, Markus Hagenbuchner
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Application of Text Mining Methodologies to Health Insurance Schedules
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Chapter 46
Miao-Ling Wang, Hsiao-Fan Wang
With the ever-increasing and ever-changing flow of information available on the Web, information analysis has never been more important. Web text... Sample PDF
Web Mining System for Mobile-Phone Marketing
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Chapter 47
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Text mining technology can be used to assist in finding relevant or novel information in large volumes of unstructured data, such as that which is... Sample PDF
Web Service Architectures for Text Mining: An Exploration of the Issues via an E-Science Demonstrator
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