@inproceedings{638b391998b04610b4d1be163e65e3c0,
title = "Statistical relational learning to recognise textual entailment",
abstract = "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.",
author = "Miguel Rios and Lucia Specia and Alexander Gelbukh and Ruslan Mitkov",
year = "2014",
doi = "10.1007/978-3-642-54906-9_27",
language = "Ingl{\'e}s",
isbn = "9783642549052",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 1",
pages = "330--339",
booktitle = "Computational Linguistics and Intelligent Text Processing - 15th International Conference, CICLing 2014, Proceedings",
address = "Alemania",
edition = "PART 1",
note = "15th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2014 ; Conference date: 06-04-2014 Through 12-04-2014",
}