UMCC-DLSI: multidimensional lexical-semantic textual similarity

Authors:
Antonio Fernández;Yoan Gutiérrez;Alexander Chávez;Héctor Dávila;Andy González;Rainel Estrada;Yenier Castañeda;Sonia Vázquez;Andrés Montoyo;Rafael Muñoz
Affiliations:
DI, University of Matanzas Autopista a Varadero, Matanzas, Cuba;DI, University of Matanzas Autopista a Varadero, Matanzas, Cuba;DI, University of Matanzas Autopista a Varadero, Matanzas, Cuba;DI, University of Matanzas Autopista a Varadero, Matanzas, Cuba;DI, University of Matanzas Autopista a Varadero, Matanzas, Cuba;DI, University of Matanzas Autopista a Varadero, Matanzas, Cuba;DI, University of Matanzas Autopista a Varadero, Matanzas, Cuba;DLSI, University of Alicante Carretera de San Vicente S/N Alicante, Spain;DLSI, University of Alicante Carretera de San Vicente S/N Alicante, Spain;DLSI, University of Alicante Carretera de San Vicente S/N Alicante, Spain
Venue:
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Year:
2012

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Abstract

This paper describes the specifications and results of UMCC_DLSI system, which participated in the first Semantic Textual Similarity task (STS) of SemEval-2012. Our supervised system uses different kinds of semantic and lexical features to train classifiers and it uses a voting process to select the correct option. Related to the different features we can highlight the resource ISR-WN used to extract semantic relations among words and the use of different algorithms to establish semantic and lexical similarities. In order to establish which features are the most appropriate to improve STS results we participated with three runs using different set of features. Our best approach reached the position 18 of 89 runs, obtaining a general correlation coefficient up to 0.72.