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This paper presents a new keyword extraction algorithm for Chinese news Web pages using lexical chains and word co-occurrence combined with frequency features, cohesion features, and corelation features. A lexical chain is an external performance consistency by semantically related words of a text, and is the
paper, we propose the automatic keyword extraction system and Thai website categorization system which can automatically update the dictionary and categorize website in Thai. The dictionary is a collection of vector which is created from the automatic keyword extraction system. The result in term of accuracy shows that our
Online advertising has now turned to be one of the major revenue sources for today's Internet companies. Among the different channels of advertising, contextual advertising takes the great part. There are already lots of studies done for the keyword extraction problem in contextual advertising for English, however
Currently, the automatic keywords extraction method can only extract keywords appeared in the articles and it cannot extract the implicit keyword which does not appear in the articles. It is a difficult work to extract implicit keywords in an article in the task of automatic keywords extraction. This work can also be
can be expected to be achieved in a QA system. Sentences are classified according to the content. Each classification is classified into a more detailed field. Important keywords are extracted from the sentences classified into the field. Moreover, the extracted keywords are classified into common and peculiar word for
In this paper we propose an approach for Chinese question analysis and answer extraction. A general question analysis process contains keyword extraction and question classification. Question classification plays a crucial role in automatic question answering. To implement the question classification, we have carried
. A third technique involves extraction of keywords and storing them in a properly indexed base. These then can serve the dual purpose of providing solutions to Lazy Learning classification for automatic subject-wise archiving and formation of relevant word sequences for detection of plagiarism using Association Rule
This paper presents a novel framework for multi-folder email classification using graph mining as the underlying technique. Although several techniques exist (e.g., SVM, TF-IDF, n-gram) for addressing this problem in a delimited context, they heavily rely on extracting high-frequency keywords, thus ignoring the
In this paper, reclassification for the current classification through K-means would be implemented based on the feedback of Web usage mining in order to improve the accuracy of news recommendation and convergence of classification. It could extract most relative keywords and eliminate the disturbance of multi-vocal
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