Abstract
N-grams based feature selection and text representation for Chinese Text Classification
Highlights
With the rapidly increasing quantity of web sources and electronic texts in Chinese, much attention has been paid to the Chinese text classification (TC)
We discussed Chinese text classification based on n-grams by using different feature selection methods and different text representation weights
In the case of using less than 3000 features, the feature selection methods based on n-gram frequency always give better results than those based on text frequency
Summary
With the rapidly increasing quantity of web sources and electronic texts in Chinese, much attention has been paid to the Chinese text classification (TC). In addition to some difficulties in text classification in English, Chinese TC exhibits the following difficulties: (1) there is no space between words in Chinese text. In a TC task, the term can be a word, a character or a n-gram. These features play the same role in Chinese TC. Unlike most of western languages, Chinese words do not have a remarkable boundary. This means that the word segmentation is necessary before any other preprocessing. Word sense disambiguation issue and unknown word recognition problem limit the precision of word segmentation
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