How preprocessing affects unsupervised keyphrase extraction

Research output: Chapter in Book/Conference paperConference paper

8 Citations (Scopus)

Abstract

Unsupervised keyphrase extraction techniques generally consist of candidate phrase selection and ranking techniques. Previous studies treat the candidate phrase selection and ranking as a whole, while the effectiveness of identifying candidate phrases and the impact on ranking algorithms have remained undiscovered. This paper surveys common candidate selection techniques and analyses the effect on the performance of ranking algorithms from different candidate selection approaches. Our evaluation shows that candidate selection approaches with better coverage and accuracy can boost the performance of the ranking algorithms. © 2014 Springer-Verlag Berlin Heidelberg.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science: Computational Linguistics and Intelligent Text Processing
Place of PublicationGermany
PublisherSpringer
Pages163-176
Volume8403
ISBN (Print)9783642549052
DOIs
Publication statusPublished - 2014
Event15th International Conference on Computational Linguistics and Intelligent Text Processing - Nepal, Kathmandu, Nepal
Duration: 6 Apr 201412 Apr 2014
Conference number: 105034

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume8403

Conference

Conference15th International Conference on Computational Linguistics and Intelligent Text Processing
Abbreviated titleCICLing 2014
CountryNepal
CityKathmandu
Period6/04/1412/04/14

Cite this

Wang, R., Liu, W., & Mcdonald, C. (2014). How preprocessing affects unsupervised keyphrase extraction. In Lecture Notes in Computer Science: Computational Linguistics and Intelligent Text Processing (Vol. 8403 , pp. 163-176). (Lecture Notes in Computer Science; Vol. 8403). Springer. https://doi.org/10.1007/978-3-642-54906-9_14