Concept-Based Text Mining

Concept-Based Text Mining

Stanley Loh (Lutheran University of Brazil, Brazil), Leandro Krug Wives (Federal University of Rio Grande do Sul, Brazil), Daniel Lichtnow (Catholic University of Pelotas, Brazil) and José Palazzo M. de Oliveira (Federal University of Rio Grande do Sul, Brazil)
Copyright: © 2009 |Pages: 13
DOI: 10.4018/978-1-59904-990-8.ch021
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Abstract

The goal of this chapter is to present an approach to mine texts through the analysis of higher level characteristics (called “concepts’), minimizing the vocabulary problem and the effort necessary to extract useful information. Instead of applying text mining techniques on terms or keywords labeling or extracted from texts, the discovery process works over concepts extracted from texts. Concepts represent real world attributes (events, objects, feelings, actions, etc.) and, as seen in discourse analysis, they help to understand ideas and ideologies present in texts. A previous classification task is necessary to identify concepts inside the texts. After that, mining techniques are applied over the concepts discovered. The chapter will discuss different concept-based text mining techniques and present results from different applications.
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Introduction

Text mining is a useful manner to examine the content of a text or a collection of texts. Many text mining approaches are based on words present in the texts or associated to them. However, such approaches are prone to suffer with the vocabulary problem. As discussed in (Chen, 1994), (Chen et al., 1996) and (Furnas, 1987), texts are written in natural language and this may cause semantic mistakes due to synonyms (different words for the same meaning), polysemy (the same word with many meanings), lemmas (words with the same radical, like the verb “to marry” and the noun “marriage”) and quasi-synonyms (words related to the same subject, object or event, like “bomb” and “terrorist attack”).

There is an approach, called concept-based, that tries to minimize such confusions. Instead of mining words, this approach, called concept-based, examines concepts present in the texts. Concepts represent real world phenomena (events, objects, subjects, feelings, actions, etc) and they help to understand ideas and ideologies present in texts.

One assumption is that a concept-based approach would minimize the vocabulary problem because concepts can be expressed with different words (synonyms), as in a semantic expansion approach, and concepts can hold:

  • a.

    Word variations: plural, gender, verbal conjugations;

  • b.

    Semantic associations: as specialization and generalizations;

  • c.

    Contextual information (or quasi-synonyms): for example “bomb” and “explosion”;

  • d.

    Semantic information: as for example “to be” versus “not to be”.

In Information Retrieval, concepts are used with success to index and retrieve documents. Lin and Chen (1996) comment “the concept-based retrieval capability has been considered by many researchers and practitioners to be an effective complement to the prevailing keyword search or user browsing”.

The goal of this chapter is to present an approach to mine texts through the analysis of high level characteristics (called “concepts’), minimizing the vocabulary problem and the effort necessary to extract useful information. Instead of applying text mining techniques on terms or keywords labeling or extracted from texts, the discovery process works over concepts extracted from texts. A pre-processing step of classification is necessary to identify concepts inside the texts. After that, mining techniques are applied over the concepts discovered.

The chapter begins discussing some related works, then presents techniques to identify concepts in the texts and mining techniques applied over concepts. The chapter ends with a conclusion and a discussion about future trends.

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Background

Feldman and partners (Feldman & Dagan, 1995) (Feldman & Hirsh, 1997) (Feldman & Dagan, 1998) face the problem of applying mining tools over keywords that are assigned to texts as attributes. These mining techniques use statistical analysis to discover association rules and interesting patterns over keyword distributions and associations. To perform the KDT process (Knowledge Discovery in Texts), keywords should be previously assigned to texts. The authors did not discuss the way in which keywords are assigned to texts, suggesting that this process may be done manually by humans or automatically by software tools. Similarly, Lin et al. (1998) use terms automatically extracted from texts to categorize documents and to find associations. The most frequent terms are assigned as keywords (attributes).

However, when analyzing terms, problems arise due to the vocabulary problem. This problem happens because the terms used by one person to describe one object, idea or situation may be different of the terms used by another person. Just to give an example, a murder may be described by one author with the term “murder” while another may use “homicide”. Thus, if we perform a mining or analysis that is based only in the terms assigned to or extracted from texts, the process may be misled by semantic gaps.

Key Terms in this Chapter

Association Rules: Rules usually in the format X ? Y, meaning that “ifXis present in an object, thenYis also present in this object“.

Concepts: Represent real world phenomena (events, objects, subjects, feelings, actions, etc) and they help to understand ideas and ideologies present in texts.

Distribution Analysis: Evaluation of the frequency of objects or attributes in a collection.

Clustering: Process that separates objects in groups (clusters) evaluating the similarity between them. The goal is to put similar objects inside the same cluster and dissimilar ones in different clusters. The number of initial clusters may not be known.

Vocabulary Problem: Problem generated by the use of natural languages and caused by semantic mistakes due to synonyms (different words for the same meaning), polysemy (the same word with many meanings), lemmas (words with the same radical, like the verb “to marry” and the noun “marriage”) and quasi-synonyms (words related to the same subject, object or event, like “bomb” and “terrorist attack”).

Semantic Expansion: A kind of technique that adds words to a set of words to better represent an object or meaning; this technique is utilized to restructure a query in information retrieval systems.

Concept-Based Text Mining: A new approach for text mining that applies statistical techniques over concepts present in texts instead of applying over words.

Temporal Analysis: Application of mining techniques on objects or events chronologically ordered, following a time sequence.

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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
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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
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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
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Featureless Data Clustering
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Chapter 10
Xiaohui Cui
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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
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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
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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
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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
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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
This chapter aims at providing an overview about the use of statistical methods supporting the Web Usage Mining. Within the first part is described... Sample PDF
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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Chapter 25
Rosa Meo, Maristella Matera
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Chapter 26
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Web Usage Mining for Ontology Management
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Chapter 27
Yue-Shi Lee
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Chapter 28
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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
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Determination of Unithood and Termhood for Term Recognition
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Chapter 31
Fotis Lazarinis
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Chapter 32
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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
Text mining is an instrumental technology that today’s organizations can employ to extract information and further evolve and create valuable... Sample PDF
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Chapter 40
E. Thirumaran
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Chapter 41
Hanna Suominen
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Performance Evaluation Measures for Text Mining
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Chapter 42
Yanliang Qi
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Text Mining in Bioinformatics: Research and Application
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Chapter 43
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Chapter 44
Richard S. Segall
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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
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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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About the Editors
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