LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

A hierarchical and parallel framework for End-to-End Aspect-based Sentiment Analysis

Photo from wikipedia

Abstract Pipeline, joint, and collapsed models are three major approaches to solving End-to-End Aspect-based Sentiment Analysis (E2E-ABSA) task. Prior works found that joint models were consistently surpassed by the other… Click to show full abstract

Abstract Pipeline, joint, and collapsed models are three major approaches to solving End-to-End Aspect-based Sentiment Analysis (E2E-ABSA) task. Prior works found that joint models were consistently surpassed by the other two. To explore the potential of joint model for E2E-ABSA, we propose a hierarchical and parallel joint framework on the basis of exploiting the hierarchical nature of the pre-trained language model and performing parallel inference of the subtasks. Our framework: (1) shares the same pre-trained backbone network between two subtasks, ensuring the associations and commonalities between them; (2) considers the hierarchical feature of the deep neural network and introduces two joint approaches, namely the specific-layer joint model and multiple-layer joint model, coupling two specific layers or multiple task-related layers with subtasks; (3) carries out parallel execution in both training and inference processes, improving the inference throughput and al-leviating the target-polarity mismatch problem. The experimental results on three benchmark datasets demonstrate that our approach outperforms the state-of-the-art works.

Keywords: end aspect; hierarchical parallel; based sentiment; aspect based; sentiment analysis; end end

Journal Title: Neurocomputing
Year Published: 2021

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.