Using Recurrent Neural Networks for Semantic Role Labeling in Portuguese

Abstract

Semantic Role Labeling is the task of automatically detecting the semantic role played by words or phrases in a sentence. There is a small number of studies dedicated to Semantic Role Labeling in the Portuguese language, and the obtained performance is far from that of the English language. In this article, we propose an end-to-end semantic role labeler for the Portuguese language, which leans on a deep bidirectional long short-term memory neural network architecture. The predictions are used as inputs to an inference stage that employs a global recursive neural parsing algorithm, tailored for the task. We also provide a detailed analysis of the effects of word embedding dimensionality and network depth on the overall performance of the proposed model. The proposed approach outperforms the state-of-the-art approach on the PropBank-Br corpus, while reducing the relative error in approximately 8.74%.