Please use this identifier to cite or link to this item:
|Title:||MFSRank: An unsupervised method to extract keyphrases using semantic information|
|Authors:||Enrique López, Roque|
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|
|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.|
|Appears in Collections:||Artículos de investigación|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.