Summary of Revisiting Structured Sentiment Analysis As Latent Dependency Graph Parsing, by Chengjie Zhou et al.
Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing
by Chengjie Zhou, Bobo Li, Hao Fei, Fei Li, Chong Teng, Donghong Ji
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed approach in this paper redefines the Structured Sentiment Analysis (SSA) task as a dependency parsing problem on partially-observed dependency trees. This allows for modeling internal structures of spans, which prior studies neglected. A two-stage parsing method is introduced, leveraging TreeCRFs with a novel constrained inside algorithm to explicitly model latent structures. The approach also incorporates joint scoring graph arcs and headed spans for global optimization and inference. Experimental results on five benchmark datasets demonstrate significant performance improvements over previous bi-lexical methods, achieving new state-of-the-art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in computer science called Structured Sentiment Analysis (SSA). People tried to solve this problem before, but they had some limitations. They didn’t consider the internal structure of sentences, and long sentences made it even harder. The authors propose a new way to solve SSA by treating it like a tree-structured problem. This allows them to consider the inner workings of sentences. They use a special algorithm that combines different types of information to make predictions. The results show that their approach is much better than previous methods. |
Keywords
» Artificial intelligence » Dependency parsing » Inference » Optimization » Parsing
