Summary of An Autoregressive Text-to-graph Framework For Joint Entity and Relation Extraction, by Urchade Zaratiana et al.
An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction
by Urchade Zaratiana, Nadi Tomeh, Pierre Holat, Thierry Charnois
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel method for joint entity and relation extraction from unstructured text is proposed, framing the task as a conditional sequence generation problem. Unlike conventional generative models, this approach uses a span-based generator that creates a linearized graph of nodes representing text spans and edges representing relation triplets. A transformer encoder-decoder architecture with a pointing mechanism on a dynamic vocabulary of spans and relation types is employed to capture structural characteristics and boundaries while grounding generated output in the original text. Competitive results are demonstrated through evaluation on benchmark datasets, with code available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to find entities and relationships in text is developed. It’s like playing a game where you find words that belong together. The method uses a special kind of computer model that looks at words and connections between them. This helps the model understand what things are and how they relate to each other. The results show this approach works well, and the code is available for others to use. |
Keywords
* Artificial intelligence * Encoder decoder * Grounding * Transformer
