Resumen
We explore various machine learning-based classifiers applied to rule-based features for recognizing textual entailment. The features, extracted with a set of synthesized matching rules, reflect syntactic and semantic similarity between the text and the hypothesis. The fact that we use only seven relatively simple features makes our method suitable for low-resource languages. We test our method on the test sets of the RTE competitions and achieve accuracy of up to 69.13%.
Idioma original | Inglés |
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Páginas (desde-hasta) | 2873-2885 |
Número de páginas | 13 |
Publicación | Journal of Intelligent and Fuzzy Systems |
Volumen | 34 |
N.º | 5 |
DOI | |
Estado | Publicada - 2018 |