期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2017
卷号：8
期号：4
DOI：10.14569/IJACSA.2017.080440
出版社：Science and Information Society (SAI)
摘要：At each text there are a few keywords which provide important information about the content of that text. Since this limited set of words (keywords) is supposed to describe the total concept of a text (e.g. article, book), the correct choosing of keywords for a text plays an important role in the right representing of that text. Despite several efforts in this field, none of the so far published methods is accurate enough to elicit representative words for retrieving a vast variety of different texts. In this study, an unsupervised scheme is proposed which is independent on domain, language, structure and length of a text. The proposed method uses the words’ frequency in conjunction with standard deviation of occurred location of words in text along with considering the conceptual relation of words. In the next stage, a secondary score is given to those selected keywords by the statistical criterion of TFISF in order to improve the basis method of TFIDF. Moreover, the proposed hybrid method does not remove the stopwords since they might be a part of bigram keywords while the similar approaches remove all stopwords at their first stage. Experimental results on the known SEMEVAL dataset imply the superiority of the proposed method in comparison with state-of-the-art schemes in terms of F-score and accuracy. Therefore, the introduced hybrid method can be considered as an alternative scheme for accurate keyword extraction.
关键词：Keyword extraction; key-phrase extraction; TFISF; standard deviation; frequency
