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Incorporating Multiple Feature Groups to a Siamese Neural Network for Semantic Textual Similarity Task in Portuguese Texts

The Semantic Textual Similarity (STS) algorithms have a key role in Natural Language Processing (NLP) studies since it can support various NLP tasks such as Text Summarization and Information Retrieval. Although we found several STS initiatives in the literature, just a few authors explored Siamese Neural Networks (SNN) to solve this problem, especially for the Portuguese language, even considering their lower need for training data and an architecture built for similarity tasks. We defined a set of lexical, semantic, distributional and graph-based feature groups to capture distinct aspects of the text and incorporated to a SNN architecture to perform STS in ASSIN 1 and ASSIN 2 datasets. The experiments indicate positive results since we improved the results of previous attempts of STS using SNNs in Portuguese texts.