Using Recurrent Neural Networks for Semantic Role Labeling in Portuguese
| Autor: | Fernando Silva Parreiras, Daniel Henrique Mourão Falci, Wladmir Cardoso Brandão, Marco Antônio Calijorne Soares |
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| Rok vydání: | 2019 |
| Předmět: |
Word embedding
Parsing Computer science business.industry Deep learning Inference 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Task (project management) Semantic role labeling Recurrent neural network 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing Sentence 0105 earth and related environmental sciences |
| Zdroj: | Progress in Artificial Intelligence ISBN: 9783030302436 EPIA (2) |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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