Abstract
In this paper, we present a similarity-based approach towards paraphrase detection in Spanish. We evaluate various models for semantic similarity computation using a gold-standard paraphrase corpus. It contains one original document and paraphrased documents on different levels (low and high), and reference documents on the same topic or same vocabulary. It allows to assess the similarity between a pair of texts or individual sentences. We found that some of the similarity metrics have a larger difference when comparing paraphrased sentences than others. Finally, we obtained a threshold for each of the similarity metrics with the aim of determining a classification boundary to decide if two sentences are paraphrased.
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Acknowledgments
This work has been partially supported by PAPIIT projects IA401219, TA100520, AG400119 and CONACYT project A1-S-27780.
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Gómez-Adorno, H., Bel-Enguix, G., Sierra, G., Torres-Moreno, JM., Martinez, R., Serrano, P. (2020). Evaluation of Similarity Measures in a Benchmark for Spanish Paraphrasing Detection. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Computational Intelligence. MICAI 2020. Lecture Notes in Computer Science(), vol 12469. Springer, Cham. https://doi.org/10.1007/978-3-030-60887-3_19
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