| Título |
SOFTCARDINALITY: Learning to Identify Directional Cross-Lingual Entailment from Cardinalities and SMT |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
SEM 2013: The Second Joint Conference on Lexical and Computational Semantics |
| Resumen |
In this paper we describe our system submitted for evaluation in the CLTE-SemEval-2013 task, which achieved the best results in two of the four data sets, and finished third in average. This system consists of a SVM classifier with features extracted from texts (and their translations SMT) based on a cardinality function. Such function was the soft cardinality. Furthermore, this system was simplified by providing a single model for the 4 pairs of languages obtaining better (unofficial) results than separate models for each language pair. We also evaluated the use of additional circular-pivoting translations achieving results 6.14% above the best official results. |
| Observaciones |
Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) |
| Lugar |
Atlanta, Georgia |
| País |
Estados Unidos |
| No. de páginas |
34-38 |
| Vol. / Cap. |
2 |
| Inicio |
2013-06-13 |
| Fin |
2013-06-14 |
| ISBN/ISSN |
978-1-937284-49-7 |
