PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Comparative Study of Features for Keyphrase Extraction
Katja Hofmann, Manos Tsagias, Edgar Meij and Maarten de Rijke
In: ACM 18th Conference on Information and Knowledge Managment (CIKM 2009), 2-6 Nov 2009, Hong Kong.


Keyphrases are short phrases that reflect the main topic of a document. Because manually annotating documents with keyphrases is a time-consuming process, several automatic approaches have been developed. Typically, candidate phrases are extracted using features such as position or frequency in the document text. Document structure may contain useful information about which parts or phrases of a document are important, but has rarely been considered as a source of information for keyphrase extraction. We address this issue in the context of keyphrase extraction from scientific literature. We introduce a new, large corpus that consists of full-text journal articles, where the rich collection and document structure available at the publishing stage is explicitly annotated. We explore features based on the XML tags contained in the documents, and based on generic section types derived using position and cue words in section titles. For XML tags we find sections, abstract, and title to perform best, but many smaller elements may be beneficial in combination with other features. Of the generic section types, the discussion section is found to be most useful for keyphrase extraction.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:6534
Deposited By:Christof Monz
Deposited On:08 March 2010