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Keyword extraction has been a very traditional topic in Natural Language Processing. However, most methods have been too complicated and slow to be applied in real applications, for example in web-based system. This paper proposes an approach which will complete some preparing works focusing on exploring the
In this paper, we tackle the problem of automatic keyword extraction in the meeting domain, a genre significantly different from written text. For the supervised framework, we proposed a rich set of features beyond the typical TFIDF measures, such as sentence salience weight, lexical features, summary sentences, and
Two keyword-extraction ways are usually used, one is simply using the information from exactly single word like word frequency and TF.IDF, the other is based on the relationship between words. The relationship is usually described as word similarity which derives from a corpus (WordNet, HowNet) or man-made thesaurus
In this paper, a method of automatic Chinese keyword extraction based on KNN is proposed. Firstly, it preprocesses the document by vector space model. Secondly, it constructs a set of candidate keywords based on KNN method and the labeled dataset. Finally, it post-processes on candidate keywords by the character of
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
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
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