A two-step convolutional neural network approach for semantic role labeling

Semantic role labeling (SRL) is a well known task in Natural Language Processing, consisting of identifying and labeling verbal arguments. It has been widely studied in English, but scarcely explored in other languages. In this paper, we employ a two-step convolutional neural architecture to label semantic arguments in Brazilian Portuguese texts, and avoid the use of external NLP tools. We achieve an F1 score of 62.2, which, although considerably lower than the state-of-the-art for English, seems promising considering the available resources. Also, dividing the process into two easier subtasks makes it more feasible to further improve performance through semi-supervised learning. Our system is available online and ready to be used out of the box to label new texts.