Research on N-grams feature selection methods for text classification
نویسندگان
چکیده
منابع مشابه
Feature Selection on Chinese Text Classification Using Character N-Grams
In this paper, we perform Chinese text classification using n-gram text representation on TanCorp which is a new large corpus special for Chinese text classification more than 14,000 texts divided into 12 classes. We use different n-gram feature (1-, 2-grams or 1-, 2-, 3-grams) to represent documents. Different feature weights (absolute text frequency, relative text frequency, absolute n-gram f...
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In this paper, text representation and feature selection strategies for Chinese text classification based on n-grams are discussed. Two steps feature selection strategy is proposed which combines the preprocess within classes with the feature selection among classes. Four different feature selection methods and three text representation weights are compared by exhaustive experiments. Both C-SVC...
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The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...
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In recent years, production of text documents has seen an exponential growth, which is the reason why their proper classification seems necessary for better access. One of the main problems of classifying text documents is working in high-dimensional feature space. Feature Selection (FS) is one of the ways to reduce the number of text attributes. So, working with a great bulk of the feature spa...
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Feature selection that aims to determine and select the distinctive terms representing a best document is one of the most important steps of classification. With the feature selection, dimension of document vectors are reduced and consequently duration of the process is shortened. In this study, feature selection methods were studied in terms of dimension reduction rates, classification success...
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ژورنال
عنوان ژورنال: IOP Conference Series: Materials Science and Engineering
سال: 2021
ISSN: 1757-8981,1757-899X
DOI: 10.1088/1757-899x/1031/1/012048