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This paper presents a corpus-based approach for extracting keywords from a text written in a language that has no word boundary. Based on the concept of Thai character cluster, a Thai running text is preliminarily segmented into a sequence of inseparable units, called TCCs. To enable the handling of a large-scaled
Internet is becoming an increasingly important platform for ordinary life and work. It is expected that keyword extraction can help people quickly find hot spots on the web, since keywords in a document provide important information about the content of the document. In this paper, we propose to use text clustering
subjectivity of deciding relevant documents empirically. Furthermore, a sentence selection strategy through extracting keywords is proposed. It calculated the word's query related feature through word co-occurrence window, and obtained the topic related feature through likelihood ratio, then combined the two features to extract
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