Using the Web as corpus for self-training text categorization

Guzmán-Cabrera, Rafael; Montes-y-Gómez, Manuel; Rosso, Paolo; Villaseñor-Pineda, Luis

doi:10.1007/s10791-008-9083-7

Cited by 15 publications

(9 citation statements)

References 18 publications

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“…In line with these current works, we have proposed a new semi-supervised method for general text classification tasks [5]. This method differs from previous approaches in two main issues.…”

Section: Introductionmentioning

confidence: 88%

Semi-supervised Word Sense Disambiguation Using the Web as Corpus

Guzmán-Cabrera

Rosso

Montes-y-Gómez

et al. 2009

Computational Linguistics and Intelligent Text Processing

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Abstract. As any other classification task, Word Sense Disambiguation requires a large number of training examples. These examples, which are easily obtained for most of the tasks, are particularly difficult to obtain for this case. Based on this fact, in this paper we investigate the possibility of using a Webbased approach for determining the correct sense of an ambiguous word based only in its surrounding context. In particular, we propose a semi-supervised method that is specially suited to work with just a few training examples. The method considers the automatic extraction of unlabeled examples from the Web and their iterative integration into the training data set. The experimental results, obtained over a subset of ten nouns from the SemEval lexical sample task, are encouraging. They showed that it is possible to improve the baseline accuracy of classifiers such as Naïve Bayes and SVM using some unlabeled examples extracted from the Web.

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“…In line with these current works, we have proposed a new semi-supervised method for general text classification tasks [5]. This method differs from previous approaches in two main issues.…”

Section: Introductionmentioning

confidence: 88%

Semi-supervised Word Sense Disambiguation Using the Web as Corpus

Guzmán-Cabrera

Rosso

Montes-y-Gómez

et al. 2009

Computational Linguistics and Intelligent Text Processing

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“…Therefore, Automatic text categorization plays an important role in helping information users overcome such a challenge by reducing the time needed to classify thousands of daily arrived documents, without the need for experts. Thus, Automatic TC can significantly reduce the cost and effort of manual categorization [3]. For example, it has been reported in the Internet World Stats (http://www.internetworldstats.com/stats7.htm) that the number of Arabic speaking Internet users has grown 2,501.2 % in the last eleven years (2000-2011), which is the highest growth rate among other languages.…”

Section: Introductionmentioning

confidence: 99%

Arabic Text Classification Algorithm using TFIDF and Chi Square Measurements

Abu-Errub¹

2014

IJCA

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Text categorization is the process of classifying documents into a predefined set of categories based on its contents of keywords. Text classification is an extended type of text categorization where the text is further categorized into subcategories. Many algorithms have been proposed and implemented to solve the problem of English text categorization and classification. However, few studies have been carried out for categorizing and classifying Arabic text. Compared to English, the Arabic text classification is considered as a very challenging due to the Arabic language complex linguistic structure and its highly derivational nature where morphology plays a very important role. This paper proposes a new method for Arabic text classification in which a document is compared with pre-defined documents categories based on its contents using the TF.IDF method (Term Frequency times Inverse Document Frequency) measure, then the document is classified into the appropriate sub-category using Chi Square measure.. General TermsInformation Retrieval.

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“…The first approach allow building a classifier by considering a small set of tagged documents along with a great number of unlabeled texts (Nigam et al, 2000;Krithara, et al, 2008;Guzmán-Cabrera et al, 2009). The second focuses on the construction of classifiers by reusing training sets from related domains (Aue and Gamon, 2005;Dai et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Using Nearest Neighbor Information to Improve Cross-Language Text Classification

Escobar-Acevedo

Montes-y-Gómez

Villaseñor-Pineda

2009

MICAI 2009: Advances in Artificial Intelligence

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Abstract. Cross-language text classification (CLTC) aims to take advantage of existing training data from one language to construct a classifier for another language. In addition to the expected translation issues, CLTC is also complicated by the cultural distance between both languages, which causes that documents belonging to the same category concern very different topics. This paper proposes a re-classification method which purpose is to reduce the errors caused by this phenomenon by considering information from the own target language documents. Experimental results in a news corpus considering three pairs of languages and four categories demonstrated the appropriateness of the proposed method, which could improve the initial classification accuracy by up to 11%.

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Using the Web as corpus for self-training text categorization

Cited by 15 publications

References 18 publications

Semi-supervised Word Sense Disambiguation Using the Web as Corpus

Semi-supervised Word Sense Disambiguation Using the Web as Corpus

Arabic Text Classification Algorithm using TFIDF and Chi Square Measurements

Using Nearest Neighbor Information to Improve Cross-Language Text Classification

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