How preprocessing affects unsupervised keyphrase extraction

Rui Wang; Wei Liu; Chris Mcdonald

doi:10.1007/978-3-642-54906-9_14

How preprocessing affects unsupervised keyphrase extraction

Rui Wang, Wei Liu, Chris Mcdonald

Graduate Research School

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

11 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 language	English
Title of host publication	Lecture Notes in Computer Science: Computational Linguistics and Intelligent Text Processing
Place of Publication	Germany
Publisher	Springer
Pages	163-176
Volume	8403
ISBN (Print)	9783642549052
DOIs	https://doi.org/10.1007/978-3-642-54906-9_14
Publication status	Published - 2014
Event	15th International Conference on Computational Linguistics and Intelligent Text Processing - Nepal, Kathmandu, Nepal Duration: 6 Apr 2014 → 12 Apr 2014 Conference number: 105034

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	8403

Conference

Conference	15th International Conference on Computational Linguistics and Intelligent Text Processing
Abbreviated title	CICLing 2014
Country/Territory	Nepal
City	Kathmandu
Period	6/04/14 → 12/04/14

Access to Document

10.1007/978-3-642-54906-9_14

Cite this

@inproceedings{fba87f47927f4eddac8dd599e4a466e6,

title = "How preprocessing affects unsupervised keyphrase extraction",

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. {\textcopyright} 2014 Springer-Verlag Berlin Heidelberg.",

author = "Rui Wang and Wei Liu and Chris Mcdonald",

year = "2014",

doi = "10.1007/978-3-642-54906-9_14",

language = "English",

isbn = "9783642549052",

volume = "8403 ",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "163--176",

booktitle = "Lecture Notes in Computer Science: Computational Linguistics and Intelligent Text Processing",

address = "Netherlands",

note = "15th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2014 ; Conference date: 06-04-2014 Through 12-04-2014",

}

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 , Lecture Notes in Computer Science, vol. 8403, Springer, Germany, pp. 163-176, 15th International Conference on Computational Linguistics and Intelligent Text Processing, Kathmandu, Nepal, 6/04/14. https://doi.org/10.1007/978-3-642-54906-9_14

How preprocessing affects unsupervised keyphrase extraction. / Wang, Rui; Liu, Wei ; Mcdonald, Chris.
Lecture Notes in Computer Science: Computational Linguistics and Intelligent Text Processing. Vol. 8403 Germany: Springer, 2014. p. 163-176 (Lecture Notes in Computer Science; Vol. 8403).

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

TY - GEN

T1 - How preprocessing affects unsupervised keyphrase extraction

AU - Wang, Rui

AU - Liu, Wei

AU - Mcdonald, Chris

N1 - Conference code: 105034

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-642-54906-9_14

DO - 10.1007/978-3-642-54906-9_14

M3 - Conference paper

SN - 9783642549052

VL - 8403

T3 - Lecture Notes in Computer Science

SP - 163

EP - 176

BT - Lecture Notes in Computer Science: Computational Linguistics and Intelligent Text Processing

PB - Springer

CY - Germany

T2 - 15th International Conference on Computational Linguistics and Intelligent Text Processing

Y2 - 6 April 2014 through 12 April 2014

ER -

How preprocessing affects unsupervised keyphrase extraction

Abstract

Publication series

Conference

Access to Document

Fingerprint

Cite this