Summary of Entity-aware Self-attention and Contextualized Gcn For Enhanced Relation Extraction in Long Sentences, by Xin Wang et al.
Entity-Aware Self-Attention and Contextualized GCN for Enhanced Relation Extraction in Long Sentences
by Xin Wang, Xinyi Bai
First submitted to arxiv on: 15 Sep 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 This paper proposes a novel relation extraction model called ESC-GCN (Entity-aware Self-attention Contextualized GCN) that incorporates syntactic structure and semantic context to improve performance. The proposed approach uses relative position self-attention to capture pairwise correlations, contextualized graph convolutional networks to prune operations and capture intra-sentence dependencies, and entity-aware attention layers to dynamically select decisive tokens. Experimental results show that ESC-GCN outperforms existing dependency-based and sequence-based models on various tasks, particularly in extracting relations between entities of long sentences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to understand how words relate to each other in text. Right now, most computers use a special kind of graph called a dependency tree to figure this out. But the authors think that these trees are missing important information from the rest of the sentence. So they created a new model that looks at the whole sentence and uses attention to focus on the parts that matter most. They tested their model on different tasks and found it worked really well, especially when dealing with long sentences. |
Keywords
» Artificial intelligence » Attention » Gcn » Self attention
