Open Access
ARTICLE
Jointly Part-of-Speech Tagging and Semantic Role Labeling Using Auxiliary Deep Neural Network Model
Yatian Shen1, Yubo Mai2, Xiajiong Shen2, Wenke Ding2, *, Mengjiao Guo3
1 School of Computer Science and Engineering, Southeast University, Nanjing, 210000, China.
2 Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, 475000, China.
3 Swinburne Data Science Research Institute, Swinburne University of Technology, Victoria, 3122, Australia.
* Corresponding Author: Wenke Ding. Email: .
Computers, Materials & Continua 2020, 65(1), 529-541. https://doi.org/10.32604/cmc.2020.011139
Received 22 April 2020; Accepted 20 May 2020; Issue published 23 July 2020
Abstract
Previous studies have shown that there is potential semantic dependency
between part-of-speech and semantic roles. At the same time, the predicate-argument
structure in a sentence is important information for semantic role labeling task. In this
work, we introduce the auxiliary deep neural network model, which models semantic
dependency between part-of-speech and semantic roles and incorporates the information
of predicate-argument into semantic role labeling. Based on the framework of joint
learning, part-of-speech tagging is used as an auxiliary task to improve the result of the
semantic role labeling. In addition, we introduce the argument recognition layer in the
training process of the main task-semantic role labeling, so the argument-related
structural information selected by the predicate through the attention mechanism is used
to assist the main task. Because the model makes full use of the semantic dependency
between part-of-speech and semantic roles and the structural information of predicateargument, our model achieved the F1 value of 89.0% on the WSJ test set of CoNLL2005,
which is superior to existing state-of-the-art model about 0.8%.
Keywords
Cite This Article
Y. Shen, Y. Mai, X. Shen, W. Ding and M. Guo, "Jointly part-of-speech tagging and semantic role labeling using auxiliary deep neural network model,"
Computers, Materials & Continua, vol. 65, no.1, pp. 529–541, 2020. https://doi.org/10.32604/cmc.2020.011139
Citations