@Article{cmc.2020.011139,
AUTHOR = {Yatian Shen, Yubo Mai, Xiajiong Shen, Wenke Ding, Mengjiao Guo},
TITLE = {Jointly Part-of-Speech Tagging and Semantic Role Labeling Using  Auxiliary Deep Neural Network Model},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {529--541},
URL = {http://www.techscience.com/cmc/v65n1/39581},
ISSN = {1546-2226},
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%.},
DOI = {10.32604/cmc.2020.011139}
}