SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Shallow semantics in fast textual entailment rule learners
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Using machine translation systems to expand a corpus in textual entailment
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
A semantic oriented approach to textual entailment using wordnet-based measures
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Using sentence semantic similarity based on WordNet in recognizing textual entailment
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
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This paper presents a system that uses machine learning algorithms for the task of recognizing textual entailment in Spanish language. The datasets used include SPARTE Corpus and a translated version to Spanish of RTE3, RTE4 and RTE5 datasets. The features chosen quantify lexical, syntactic and semantic level matching between text and hypothesis sentences. We analyze how the different sizes of datasets and classifiers could impact on the final overall performance of the RTE classification of two-way task in Spanish. The RTE system yields 60.83% of accuracy and a competitive result of 66.50% of accuracy is reported by train and test set taken from SPARTE Corpus with 70% split.