Abstract

Reorganizing words in a passage using synonyms and different words without changing the main message delivered in the original sentence is called paraphrasing. Simplifying, clarification or taking quotes, etc. In this paper, we address a Paraphrase Identification model for Mexican Spanish text pairs. A data augmentation step was done using Google Translate API, and then three different similarity algorithms, namely: Jaccard, Cosine, and Spacy similarity were used to create a similarity vector for each text pair. The paraphrase identification task was modeled as binary classification of text pairs into two classes, namely: Paraphrases and Not-Paraphrases. The proposed methodology with voting classifier of three machine learning classifiers obtained a F1-score of 0.8754 for paraphrases category.

Keywords

Paraphrase, Spanish, Similarity, Data Augmentation

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Recommended citation:

@inproceedings{ArifEtAl:CLEF2022,
title = {},
author = {Abdul Gafar Manuel Meque and Fazlourrahman Balouchzahi and Grigori Sidorov and Alexander Gelbukh},
pages = {--},
url = {http://ceur-ws.org/},
crossref = {IBERLEF2022},
}