Automatic Text Classification: A Technical Review

Dalal, Mita K.; Zaveri, Mukesh A.

doi:10.5120/3358-4633

Cited by 113 publications

(65 citation statements)

References 30 publications

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“…Note that, on the BBC dataset, even the memory was extended, Weka ran out of memory in the training process of SVM when 30% of features were selected. 4 Hence, there is no report of the case in Table 3. Table 3.…”

Section: Resultsmentioning

confidence: 96%

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A Framework for Text Classification Using Intuitionistic Fuzzy Sets

Intarapaiboon

2015

Lecture Notes in Electrical Engineering

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Abstract. Due to massively increasing of web pages and online documents, one of crucial processes to handle those documents is automatic (or at least semiautomatic) text classification. Based on the concept of intuitionistic fuzzy set (IFS), a framework for text classification is presented. In the framework, we introduce statistical methods to represent each document as an IFS and to learn a pattern of each document class. Then, a similarity measure for IFSs is applied in order to assign the most relevant class to a new document. The proposed framework with various similarity measures for IFSs is evaluated by benchmark datasets. The experimental results show that our framework yields satisfactory results.

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Section: Resultsmentioning

confidence: 96%

“…Reviews of recent research on TC using those classification techniques can be found in several articles, e.g. [1], [2], [3], [4], [5]. Recently, intuitionistic fuzzy set (IFS) [6] has been much explored in both theory and application.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Text Classification Using Intuitionistic Fuzzy Sets

Intarapaiboon

2015

Lecture Notes in Electrical Engineering

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“…As facing the challenge of ever-growing textual materials in the internet, the automatic text classification is viewed as one of the significant techniques to facilitate the information access of textual resources (Dalal and Zaveri, 2011). Among them, the e-book, shorten for the electronic book, refers to textual materials delivered in patterns within discourse segments can be detected and collected to perform the text classification.…”

Section: Introductionmentioning

confidence: 99%

A feature selection approach for automatic e-book classification based on discourse segmentation

Guo

Wang

Lai

2015

Program

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Purpose -The purpose of this paper is to develop a novel feature selection approach for automatic text classification of large digital documentse-books of online library system. The main idea mainly aims on automatically identifying the discourse features in order to improving the feature selection process rather than focussing on the size of the corpus. Design/methodology/approach -The proposed framework intends to automatically identify the discourse segments within e-books and capture proper discourse subtopics that are cohesively expressed in discourse segments and treating these subtopics as informative and prominent features. The selected set of features is then used to train and perform the e-book classification task based on the support vector machine technique. Findings -The evaluation of the proposed framework shows that identifying discourse segments and capturing subtopic features leads to better performance, in comparison with two conventional feature selection techniques: TFIDF and mutual information. It also demonstrates that discourse features play important roles among textual features, especially for large documents such as e-books. Research limitations/implications -Automatically extracted subtopic features cannot be directly entered into FS process but requires control of the threshold. Practical implications -The proposed technique has demonstrated the promised application of using discourse analysis to enhance the classification of large digital documentse-books as against to conventional techniques. Originality/value -A new FS technique is proposed which can inspect the narrative structure of large documents and it is new to the text classification domain. The other contribution is that it inspires the consideration of discourse information in future text analysis, by providing more evidences through evaluation of the results. The proposed system can be integrated into other library management systems.2 PROG 49,1 digitalized format, which provides several benefits to readers, including shortened publication cycles, faster distribution channels that permit the wider propagation of timely information, and friendly visualization to display deliberate content of articles. As regarding the essence of content, e-book is different from other type of textual materials because it usually contains lengthy content which easily leads to higher feature dimension in the perspective of term-level analysis. As such, the book may consist of a variety of themes in the text stream, which are distributed and shifted among sentences or paragraphs in turns of the hidden subtopic to stretch the expression of the main topic (Hearst, 1997;Ridel and Bieman, 2012). Such phenomena mostly addressed in discourse analysis raises interesting issues to text processing problems for which an innovative strategy is needed as exploring the high-level linguistic attributes from e-book.The properties sustaining the lexical coherence of topic imply the flow of discourse information, which is valuable to be further investigated an...

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“…Reviews of recent research on TC using those classification techniques can be found in several articles, e.g. [14], [15], [16], [17], [18]. To the best of our knowledge, very few works on applying IFS to TC were reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

An application of intuitionistic fuzzy sets in text classification

Intarapaiboon

2014

2014 International Conference on Information Science, Electronics and Electrical Engineering

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Intuitionistic fuzzy set (IFS) is an extended version of fuzzy set being capable of representing hesitancy degrees. Based on similarity measures for IFSs, a framework for text categorization is presented. Two main challenges are addressed: one is how to represent documents in terms of IFSs; the other is how to learn a pattern of each category from such IFS-based representation. As an exploratory study, the proposed framework is applied to a benchmark data set for text categorization. By using some existing similarity measures for IFSs, the experimental results show that the proposed framework yields satisfactory results.____________________________________ 978-1-4799-3197-2/14/$31.00 ©2014 IEEE

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Automatic Text Classification: A Technical Review

Cited by 113 publications

References 30 publications

A Framework for Text Classification Using Intuitionistic Fuzzy Sets

A Framework for Text Classification Using Intuitionistic Fuzzy Sets

A feature selection approach for automatic e-book classification based on discourse segmentation

An application of intuitionistic fuzzy sets in text classification

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