Keyword extraction explained

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

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 discussed in a document. The task of keyword extraction is an important problem in text mining, information extraction, information retrieval and natural language processing (NLP).[3]

Keyword assignment vs. extraction

Keyword assignment methods can be roughly divided into:

Methods for automatic keyword extraction can be supervised, semi-supervised, or unsupervised.[4] Unsupervised methods can be further divided into simple statistics, linguistics or graph-based, or ensemble methods that combine some or most of these methods. [5]

References

  1. An Overview of Graph-Based Keyword Extraction Methods and Approaches. . Beliga, Slobodan . Ana, Meštrović . Martinčić-Ipšić, Sanda. . Journal of Information and Organizational Sciences. . 39 . 2015 . 1. 1–20.
  2. TextRank: Bringing Order into Texts . Rada Mihalcea . Paul Tarau . Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2004) . Barcelona, Spain . July 2004.
  3. Toward Selectivity-Based Keyword Extraction for Croatian News . Beliga, Slobodan . Meštrović, Ana . Martinčić- Ipšić, Sanda. . Surfacing the Deep and the Social Web (SDSW 2014) . Italy . CEUR Proc. . 1310 . 2014 . 1–14.
  4. Alrehamy, H. . Walker, C. . SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation . 17th UK Workshop on Computational Intelligence. 2017.
  5. Tayfun Pay . Stephen Lucci . Automatic Keyword Extraction: An Ensemble Method . 2017 IEEE International Conference on Big Data (Big Data) . 2017. 10.1109/BigData.2017.8258552 .