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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15890
Title: MFSRank: An unsupervised method to extract keyphrases using semantic information
Authors: Enrique López, Roque
Barreda, Dennis
Tejada, Javier
Cuadros Vargas, Ernesto
Keywords: External resources;F-score;Graph-based methods;Keyphrase extraction;Maximal frequent sequences;PageRank algorithm;Semantic Graphs;Semantic information;Semantic relatedness;Statistical information;Two stage;Unsupervised method;Weight values;Artificial intelligence;Information use;Semantics
Issue Date: 2011
Publisher: Scopus
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-82555191211&doi=10.1007%2f978-3-642-25324-9_29&partnerID=40&md5=0cbe140efeaf7b137e027d81c8f015ac
Abstract: This paper presents an unsupervised graph-based method to extract keyphrases using semantic information. The proposed method has two stages. In the first one, we have extracted MFS (Maximal Frequent Sequences) and built the nodes of a graph with them. The weight of the connection between two nodes has been established according to common statistical information and semantic relatedness. In the second stage, we have ranked MFS with traditionally PageRank algorithm; but we have included ConceptNet. This external resource adds an extra weight value between two MFS. The experimental results are competitive with traditional approaches developed in this area. MFSRank overcomes the baseline for top 5 keyphrases in precision, recall and F-score measures. © 2011 Springer-Verlag.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15890
ISBN: urn:isbn:9783642253232
ISSN: 3029743
Appears in Collections:Artículos - Ciencia de la computación

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