Recognizing textual entailment using a machine learning approach
MA Ríos Gaona, A Gelbukh… - Advances in Soft …, 2010 - Springer
MA Ríos Gaona, A Gelbukh, S Bandyopadhyay
Advances in Soft Computing: 9th Mexican International Conference on Artificial …, 2010•SpringerWe present our experiments on Recognizing Textual Entailment based on modeling the
entailment relation as a classification problem. As features used to classify the entailment
pairs we use a symmetric similarity measure and a non-symmetric similarity measure. Our
system achieved an accuracy of 66% on the RTE-3 development dataset (with 10-fold cross
validation) and accuracy of 63% on the RTE-3 test dataset.
entailment relation as a classification problem. As features used to classify the entailment
pairs we use a symmetric similarity measure and a non-symmetric similarity measure. Our
system achieved an accuracy of 66% on the RTE-3 development dataset (with 10-fold cross
validation) and accuracy of 63% on the RTE-3 test dataset.
Abstract
We present our experiments on Recognizing Textual Entailment based on modeling the entailment relation as a classification problem. As features used to classify the entailment pairs we use a symmetric similarity measure and a non-symmetric similarity measure. Our system achieved an accuracy of 66% on the RTE-3 development dataset (with 10-fold cross validation) and accuracy of 63% on the RTE-3 test dataset.
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