Semantic Textual Similarity: In Defense of Wordnet-Based Methods - Gonçalves, Eduardo Corrêa (Anais do Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL); 2023: Anais do XIV Simpósio Brasileiro de Tecnologia da Informação e da Linguagem HumanaDO - 10.5753/stil.2023.233464, 2023)
Wordnets have long been used as a tool for evaluating the semantic similarity between short texts. In addition to being simpler than recent deep learning approaches, methods based on wordnets offer an important advantage: they deliver results that are easy to interpret as their decisions are usually taken by considering the proximity between graph nodes. In this work, we explore a lightweight approach based on a Portuguese wordnet to solve the ASSIN 2 Semantic Textual Similarity (STS) shared task. In this task, each object of a dataset consists of a pair of Portuguese sentences annotated with its semantic score and the goal is to learn an STS model to estimate the similarity value of new, previously unseen, sentence pairs. Experiments show that our results are competitive with state-of-the-art methods in terms of mean squared error.