Work

USSA: A Unified Table Filling Scheme for Structured Sentiment Analysis

Year: 2023

Type: conference-paper

Abstract: Most previous studies on Structured Sentiment Analysis (SSA) have cast it as a problem of bi-lexical dependency parsing, which cannot address issues of overlap and discontinuity simultaneously.In this paper, we propose a nichetargeting and effective solution.Our approach involves creating a novel bi-lexical dependency parsing graph, which is then converted to a unified 2D table-filling scheme, namely USSA.The proposed scheme resolves the kernel bottleneck of previous SSA methods by utilizing 13 different types of relations.In addition, to closely collaborate with the USSA scheme, we have developed a model that includes a proposed bi-axial attention module to effectively capture the correlations among relations in the rows and columns of the table.Extensive experimental results on benchmark datasets demonstrate the effectiveness and robustness of our proposed framework, outperforming state-ofthe-art methods consistently 1 .

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/2023.acl-long.802

Authors: Zepeng Zhai, Hao Chen, Ruifan Li, Xiaojie Wang

Institutions: Beijing University of Posts and Telecommunications, Ministry of Education of the People's Republic of China, Ministry of Culture and Tourism of the People's Republic of China

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Natural Science Foundation of China

Award: G1049711278