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Keyword extraction is tasked with the automatic identification of terms that best describe the subject of a document.〔(), Beliga, Slobodan; Ana, Meštrović; Martinčić-Ipšić, Sanda. An Overview of Graph-Based Keyword Extraction Methods and Approaches. // Journal of Information and Organizational Sciences. 39 (2015), 1; 1-20.〕 〔(), Rada Mihalcea and Paul Tarau, TextRank: Bringing Order into Texts, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), Barcelona, Spain, July 2004.〕 ''Key phrases'', ''key terms'', ''key segments'' or just ''keywords'' are the terminology which is used for defining the terms that represent the most relevant information contained in the document. Although the terminology is different, function is the same: characterization of the topic discused in a document. Keyword extraction task is important problem in Text Mining, Information Retrieval and Natural Language Processing.〔(), Beliga, Slobodan; Meštrović, Ana; Martinčić- Ipšić, Sanda. Toward Selectivity-Based Keyword Extraction for Croatian News // Surfacing the Deep and the Social Web (SDSW 2014). Italy : CEUR Proc. vol. 1310, 2014. 1-14.〕 ==Keyword assignment vs. extraction== Keyword assignment methods can be roughly divided into: * keyword assignment (keywords are chosen from controlled vocabulary or taxonomy) and * keyword extraction (keywords are chosen from words that are explicitly mentioned in original text). Methods for automatic keyword extraction can be: * supervised, * semi-supervised and * unsupervised. Unsupervised methods can be further divided into: *simple statistics, *linguistics, *graph-based and *other methods. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Keyword extraction」の詳細全文を読む スポンサード リンク
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