Improving Aspect Extraction based on Rules through Deep Syntax-Semantics Communication

Anonymous

Abstract: Recent studies show integrating language resources which consist of lexical resources, syntactic resources and semantic resources can improve the performance of natural language processing (NLP) tasks. The existing methods mostly perform simple integration through concatenating these resources successively, seldom consider complementary relationship among them, such as the deep communication of syntactic and semantic relations between words. To enhance deep syntax-semantics communication, this paper takes aspect term extraction (ATE) task as an example and explores four integration strategies of language resources. These strategies, based on Answer Set Programming (ASP) rules, have interpretability. Experiments on eight ATE datasets show that our strategies achieve superior performance, demonstrating that they are highly effective in integrating language resources.