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 | |
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 |