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C-rank: a concept linking approach to unsupervised keyphrase extraction
DALLE LUCCA TOSI, Mauro; Reis, Julio Cesar Dos
2019In Research Conference on Metadata and Semantics Research
Peer reviewed
 

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Abstract :
[en] Keyphrase extraction is the task of identifying a set of phrases that best represent a natural language document. It is a fundamental and challenging task that assists publishers to index and recommend relevant documents to readers. In this article, we introduce C-Rank, a novel unsupervised approach to automatically extract keyphrases from single documents by using concept linking. Our method explores Babelfy to identify candidate keyphrases, which are weighted based on heuristics and their centrality inside a co-occurrence graph where keyphrases appear as vertices. It improves the results obtained by graph-based techniques without training nor background data inserted by users. Evaluations are performed on SemEval and INSPEC datasets, producing competitive results with state-of-the-art tools. Furthermore, C-Rank generates intermediate structures with semantically annotated data that can be used to analyze larger textual compendiums, which might improve domain understatement and enrich textual representation methods.
Disciplines :
Computer science
Author, co-author :
DALLE LUCCA TOSI, Mauro  ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Reis, Julio Cesar Dos
External co-authors :
yes
Language :
English
Title :
C-rank: a concept linking approach to unsupervised keyphrase extraction
Publication date :
2019
Event name :
13th International Conference on Metadata and Semantics Research
Event organizer :
Springer
Event date :
from 28-10-2019 to 31-10-2019
Audience :
International
Main work title :
Research Conference on Metadata and Semantics Research
Pages :
236--247
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 06 September 2022

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