Abstract
This paper presents a Recognizing Textual Entailment system which uses semantic distances to sentence level over WordNet to assess the impact on predicting Textual Entailment datasets. We extent word-to-word metrics to sentence level in order to best fit in textual entailment domain. Finally, we show experiments over several RTE datasets and draw conclusions about the useful of WordNet semantic measures on this task. As a conclusion, we show that an initial but average-score system can be built using only semantic information from WordNet.
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Castillo, J.J., Cardenas, M.E. (2010). Using Sentence Semantic Similarity Based on WordNet in Recognizing Textual Entailment. In: Kuri-Morales, A., Simari, G.R. (eds) Advances in Artificial Intelligence – IBERAMIA 2010. IBERAMIA 2010. Lecture Notes in Computer Science(), vol 6433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16952-6_37
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DOI: https://doi.org/10.1007/978-3-642-16952-6_37
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