Affiliations: [a] School of Mathematics Sciences, Huaqiao University, Quanzhou 362021, Fujian, China | [b] Cognitive Science Department, Xiamen University, Xiamen 361005, Fujian, China
Correspondence:
[*]
Corresponding author: Zhehuang Huang, School of Mathematics Sciences, Huaqiao University, Quanzhou 362021, Fujian, China. E-mail:hzh98@hqu.edu.cn
Abstract: Semantic role labeling (SRL) is a key problem in natural language processing
which goal is to find a sentence-level semantic representation. Word sense
information plays an important role on the determination of semantic roles.
The introduction of word sense in the process of semantic role labeling will
hopefully lead to achieve better result. But how to better reflect the
relationship between word sense information and semantic role information is
a key task. Synergetic neural network (SNN) provides an opportunity for us
to study how to use word sense for semantic role labeling. The role labeling
process can be seen as a competition process of many roles chain order
parameters with word sense, of which order parameter with the largest
support will win, thereby obtaining desired pattern. There are three main
contributions in this article: firstly, we introduce synergetic theory to
semantic analysis and propose a semantic analysis method based on synergetic
neural network, which can effectively use semantic information and word
sense information. Secondly, fluctuating force is introduced into potential
evolution function which can effectively make use of prior semantic
knowledge. Finally, we use artificial fish swarm algorithm (AFSA) to realize
the optimization of network parameter which has both global and local search
ability, and not easy to fall into local extremism. Experiment results show
the proposed model in this paper can further improve the performance of
semantic role labeling, and thus provides an important reference value to
future research.
Keywords: Semantic role labeling (SRL), synergetic neural network (SNN), fluctuating force, evolution equation reconstruction, artificial fish swarm algorithm (AFSA)
