Recognizing Textual Entailment in Vietnamese Text: An Experimental Study

This paper proposes a model which utilizes Support Vector Machines (SVMs) - a machine learning approach for recognizing textual entailment in Vietnamese text, including three steps: (1) feature extraction, (2) training and (3) judgement by voting. In the first step, many features (e.g., Euclidean distance, Cosine, if-idf, etc) were extracted to train three classification models for the second step. The final step judged whether there is an entailment relation between a text and a hypothesis (another text can be plausibly inferred from the original one) or not. To improve the recognition quality, a combination of classifiers was proposed under voting method as human judgement on Vietnamese version of RTE-3. By using voting, our approach obtained significant improvements (from 1.2% to 9.4% of F-score) in comparison with baselines and ensemble methods, e.g. AdaBoost and Bagging.