| Título |
Statistical relational learning to recognise textual entailment |
| Tipo |
Congreso |
| Sub-tipo |
SCOPUS |
| Descripción |
Lecture Notes in Computer Science; 15th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2014 |
| Resumen |
We propose a novel approach to recognise textual entailment (RTE) following a two-stage architecture - alignment and decision - where both stages are based on semantic representations. In the alignment stage the entailment candidate pairs are represented and aligned using predicate-argument structures. In the decision stage, a Markov Logic Network (MLN) is learnt using rich relational information from the alignment stage to predict an entailment decision. We evaluate this approach using the RTE Challenge datasets. It achieves the best results for the RTE-3 dataset and shows comparable performance against the state of the art approaches for other datasets. |
| Observaciones |
(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Code 105034 |
| Lugar |
Kathmandu |
| País |
Nepal |
| No. de páginas |
330-339 |
| Vol. / Cap. |
Vol. 8403 LNCS, Issue PART 1 |
| Inicio |
2014-04-06 |
| Fin |
2014-04-12 |
| ISBN/ISSN |
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