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Text categorization has become one of the key techniques for handling and organizing text data. In practical text classification tasks, the ability to interpret the classification result is as important as the ability to classify exactly. Associative classifiers have many favorable characteristics such as rapid training, good classification accuracy, and excellent interpretation. In this paper, Closed-AC,...
Chinese segmentation is an important issue in Chinese text processing. The traditional segmentation methods those depend on an existing dictionary suffer the drawbacks when encounter unknown words. The paper proposed a segmenting algorithm for Chinese based on extracting local context information. It added the context information of the testing text into the local PPM statistical model so as to guide...
Anaphora is a common phenomenon in a natural language. It plays a large role in the coherence of a text and is a subject of active study in computational linguistics. This paper puts forward several anaphora resolution algorithms for the personal pronouns of written Chinese based on Focus-set Theory and DRT. The strength of our methods lies in the emphasis on the construction of Focus-set and in the...
The feature extraction is the most key technology of text categorization. The word is used as the feature in the traditional text classification, and its effect for the text classification is evidence. The feature extraction method using base phrase and keyword changes the feature extraction of Chinese text from syntax and semantic further. In the first, analyzing the feature of baseNP and basedVP,...
In this paper, we present an automatic terminology extraction approach for Chinese multi-word terms. In this term extraction system, besides five linguistic rules acquired from an available term list by some machine learning methods, two statistical strategies are involved: a termhood measure based on the term distribution variation, and a unithood measure adopting the left and right entropy method...
Existing methods for text categorization assume that a document is a bag of words. While computationally efficient, such a representation is unable to capture sequential information. In this paper, a document is looked upon as a sequence of characters or words and the preprocessing for text categorization, such as word segmentation and feature selection, is not demanded. Statistical dependencies among...
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