Summary of Extensible Multi-granularity Fusion Network For Aspect-based Sentiment Analysis, by Xiaowei Zhao et al.
Extensible Multi-Granularity Fusion Network for Aspect-based Sentiment Analysis
by Xiaowei Zhao, Yong Zhou, Xiujuan Xu, Yu Liu
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Aspect-based Sentiment Analysis (ABSA) evaluates sentiment expressions within a text to comprehend sentiment information. The field has seen advancements with the integration of external knowledge, such as knowledge graphs, to enhance semantic features in ABSA models. Recent studies have utilized Graph Neural Networks (GNNs) on dependency and constituent trees for syntactic analysis. However, this incorporation of diverse linguistic and structural features introduces complexity and confusion. A scalable framework for integrating these features into ABSA does not yet exist. This paper addresses this gap by presenting the Extensible Multi-Granularity Fusion (EMGF) network, which combines information from dependency and constituent syntactic, attention semantic, and external knowledge graphs. EMGF’s multi-anchor triplet learning and orthogonal projection enable efficient harnessing of combined feature potential without additional computational expenses. Experimental results on SemEval 2014 and Twitter datasets confirm EMGF’s superiority over existing ABSA methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ABSA helps us understand how people feel about certain things in text. It gets better when it uses extra information like knowledge graphs. Researchers have also used special computer models called Graph Neural Networks to study the structure of sentences. But using all these different features makes it hard for computers to do their job well. To fix this, scientists developed a new way to combine all these features together called Extensible Multi-Granularity Fusion (EMGF). EMGF uses special techniques to make sure all the information works together without making things too complicated. Tests on real data showed that EMGF is better than other ways of doing ABSA. |
Keywords
» Artificial intelligence » Attention
