Journal of Applied Science and Engineering

Published by Tamkang University Press


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Wenting FanThis email address is being protected from spambots. You need JavaScript enabled to view it.

School of European Language and Culture Studies, Dalian University of Foreign Languages Dalian 116044, Liaoning, China



Received: November 27, 2023
Accepted: December 25, 2023
Publication Date: March 27, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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The traditional semantic role labeling methods mostly use the combination of machine learning and feature engineering. In this kind of method, it usually relies on the artificially extracted features, and will bring problems such as complex model and sparse features. Semantic role labeling has strong dependence on syntax. The traditional CNN model is limited by the convolution kernel receptive field and can not get the global information well. Therefore, in this paper we propose a Portuguese framework semantic role labeling based on multiple attention mechanisms and Bi-LSTM. The multiple attention mechanism is introduced to capture the syntactic information of each word in the sentence. The bidirectional LSTM layer and CRF layer are added to construct a sequence labeling model, which considers context information, lexical information and frame type information at the same time. The experimental results show that the F1 value (exceeding 83%) of the model is improved on the Portuguese data set, which proves that the inclusion of the multiple attention mechanisms can improve the performance of the framework semantic role labeling model.

Keywords: Semantic role labeling; CNN model; Multiple attention mechanisms; Bi-LSTM; CRF layer

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