A synergetic semantic role labeling model with the introduction of fluctuating force accompanied with word sense information

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.