Domain-specific keyphrase extraction

By Yi-fang Brook Wu, Quanzhi Li, R. Bot, Xin Chen. International Conference on Information and Knowledge Management, 2005

455 citations recorded by the source corpus.

Document keyphrases provide semantic metadata characterizing documents and producing an overview of the content of a document. They can be used in many text-mining and knowledge management related applications. This paper describes a Keyphrase Identification Program (KIP), which extracts document keyphrases by using prior positive samples of human identified domain keyphrases to assign weights to the candidate keyphrases. The logic of our algorithm is: the more keywords a candidate keyphrase contains and the more significant these keywords are, the more likely this candidate phrase is a keyphrase. To obtain prior positive inputs, KIP first populates its glossary database using manually identified keyphrases and keywords. It then checks the composition of all noun phrases of a document, looks up the database and calculates scores for all these noun phrases. The ones having higher scores will be extracted as keyphrases.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/2d7f212d9251afc479d9a67570d5e0a5b4337d04

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In Artificial Intelligence

Citations in the corpus

SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles (2010, methodology): “Traditionally, automatic keyphrase extraction systems have been assessed using the proportion of top-N candidates that exactly match the goldstandard keyphrases (Frank et al., 1999; Witten et al., 1999; Turney, 1999).”
Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis (2020, methodology): “KEA was developed based on the work of Turney (2002) and was programmed in the Java language; it is a simple and efficient two-step algorithm that can be used across numerous platforms (Frank et al., 1999).”
Human-competitive tagging using automatic keyphrase extraction (2009, methodology): “Kea (Frank et al. , 1999) uses the Naïve Bayes classifier, which implicitly assumes that the features are independent of each other given the classification.”
Finding advertising keywords on web pages (2006, methodology): “We compared these approaches to several di.erent baseline approaches, including a traditional TF×IDF model; a model using TF and IDF features but with learned weights; and the KEA system [7].”
Deep Keyphrase Generation (2017, methodology): “The second step is to rank candidates on their importance to the document, either through supervised or unsupervised machine learning methods with a set of manually-defined features (Frank et al., 1999; Liu et al., 2009, 2010; Kelleher and Luz, 2005; Matsuo and Ishizuka, 2004; Mihalcea and Tarau, 20”