Affective Commonsense Knowledge Enhanced Dependency Graph for aspect sentiment triplet extraction

Abstract

Most existing aspect sentiment triplet extraction models emphasize the adoption of novel tagging scheme to jointly extract three elements of sentiment triplets, but they overlook the intrinsic information of individual words, including the implicit relationships between words, which results in the inaccurate triplet extraction. In this paper, we propose a novel method named Affective Commonsense Knowledge Enhanced Dependency Graph, which considers the sentiment information contained in each word and the relative positional distance relationships between them. Specifically, a dependency graph is generated, incorporating the sentiment information obtained through an external knowledge base called SenticNet. This dependency graph is integrated into a multi-layer Graph Convolutional Network to enhance the sentiment dependency relationships between words. Additionally, an attention mechanism with relative position embeddings is utilized to acquire word representations which incorporate contextual and syntactic information. Furthermore, an expanded Grid Tagging Scheme is employed to provide more accurate description of the relationships between words. Experimental results on SemEval and ASTE-Data-V2 demonstrate its superior performance of our method.

Three-stage document-level entity relation extraction

“…The size of the sliding window before and after the original model is (W − n)/2 words, where n is the length of the text and W is a fixed value. In this paper, a sliding window with fixed length n and unit sentence is selected to record all sentence subscripts obtained on the complex relation reasoning path to form a set Sun et al (2024). The subscripts of n sentences before and after each sentence are added to the evidence sentence and duplicated are removed.…”