Keyword extraction

From Wikipedia, the free encyclopedia

Keyword extraction is tasked with the automatic identification of terms that best describe the subject of a document.[1] [2]

Key phraseskey termskey 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 discussed in a document. Keyword extraction task is important problem in Text MiningInformation Retrieval and Natural Language Processing.[3]

Keyword assignment vs. extraction[edit]

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, or unsupervised.[4] Unsupervised methods can be further divided into simple statistics, linguistics, graph-based, and other methods.

References[edit]

  1. Jump up^ Beliga, Slobodan; Ana, Meštrović; Martinčić-Ipšić, Sanda. (2015). "An Overview of Graph-Based Keyword Extraction Methods and Approaches."Journal of Information and Organizational Sciences39 (1): 1–20.
  2. Jump up^ Rada Mihalcea and Paul Tarau (July 2004). TextRank: Bringing Order into Texts (PDF). Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2004). Barcelona, Spain.
  3. Jump up^ Beliga, Slobodan; Meštrović, Ana; Martinčić- Ipšić, Sanda. (2014). Toward Selectivity-Based Keyword Extraction for Croatian News (PDF). Surfacing the Deep and the Social Web (SDSW 2014). 1310,. Italy: CEUR Proc. pp. 1–14.
  4. Jump up^ Alrehamy, H.; Walker, C. (2017). SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation. 17th UK Workshop on Computational Intelligence.

 
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resource: wikipedia