Summary of Se-gcl: An Event-based Simple and Effective Graph Contrastive Learning For Text Representation, by Tao Meng et al.
SE-GCL: An Event-Based Simple and Effective Graph Contrastive Learning for Text Representation
by Tao Meng, Wei Ai, Jianbin Li, Ze Wang, Yuntao Shou, Keqin Li
First submitted to arxiv on: 16 Dec 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 an event-based graph contrastive learning (SE-GCL) approach for natural language processing, which addresses the limitations of current mainstream graph contrastive learning methods. SE-GCL extracts event blocks from text and constructs internal relation graphs to capture complex semantic information. The framework uses a streamlined, unsupervised graph contrastive learning process that leverages the complementary nature of event semantic and structural information. The paper introduces an event skeleton concept for core representation semantics and simplifies data augmentation techniques to boost algorithmic efficiency. Experimental results on four standard datasets (AG News, 20NG, SougouNews, and THUCNews) demonstrate the effectiveness of SE-GCL in text representation learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better from text by using a new way to represent words and sentences. Current methods are good, but they need special knowledge or extra work to be useful. The new method, called SE-GCL, takes out important parts of the text and connects them to get at the meaning. It’s like looking at pictures of events to understand what’s happening. This makes it easier and faster to learn from text. The paper shows that this new way works well on four different datasets. |
Keywords
» Artificial intelligence » Data augmentation » Natural language processing » Representation learning » Semantics » Unsupervised
