Impact of Text Specificity and Size on Word Embeddings Performance: An Empirical Evaluation in Brazilian Legal Domain

Abstract

Word embeddings is a text representation technique capable of capturing syntactic and semantic linguistic patterns and of representing each word as an n-dimensional dense vector. In the domain of legal texts, there are trained word embeddings in languages like English, Polish, and Chinese. However, to the best of our knowledge, there are no embeddings based on Portuguese (Brazilian and European) legal texts. Given that, our research question is: does the specificity and size of the text corpus used for a word embedding training contribute to a more successful classification? To answer the question, we train word embeddings models in the legal domain with different levels of specificity and size. Then we evaluate their impact on text classification. To deal with the different levels of specificity, we collect text documents from different courts of the Brazilian Judiciary, in hierarchical order. We used these text corpora to train a word embeddings model (GloVe) and then had then evaluated while classifying processes with a deep learning model (CNN). In a context perspective, the results show that in word embeddings trained on smaller corpora sizes, text specificity has a higher impact than for large sizes. Also, in a corpus size perspective, the results demonstrate that the greater the corpus size in embeddings training, the better are the results. However, this impact decreases as the corpus size increases until a point where more words in the corpus have little impact on the results.