Summary of Chem-finese: Validating Fine-grained Few-shot Entity Extraction Through Text Reconstruction, by Qingyun Wang et al.
Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction
by Qingyun Wang, Zixuan Zhang, Hongxiang Li, Xuan Liu, Jiawei Han, Huimin Zhao, Heng Ji
First submitted to arxiv on: 18 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 The paper proposes a novel approach called Chem-FINESE for fine-grained few-shot entity extraction in the chemical domain. The challenge is two-fold: handling sentences with more entities than general texts, and extracting long-tailed entity types. Chem-FINESE consists of a sequence-to-sequence (seq2seq) entity extractor and a self-validation module that reconstructs the input sentence from extracted entities. A new contrastive loss reduces excessive copying during extraction. The framework is evaluated on two datasets, achieving up to 8.26% and 6.84% absolute F1-score gains respectively. The proposed approach addresses the unique challenges of fine-grained few-shot entity extraction in the chemical domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Chem-FINESE is a new way to help computers find specific words or phrases in chemical text, like names of molecules or compounds. This task is tricky because chemical texts often have many more specific words than regular texts. The approach uses two parts: one that extracts the important words and another that makes sure those extracted words are correct by trying to recreate the original sentence from them. A new way to measure how well this works, called contrastive loss, helps prevent the computer from just copying parts of the text instead of really understanding it. The results show that Chem-FINESE can greatly improve accuracy on certain types of chemical texts. |
Keywords
* Artificial intelligence * Contrastive loss * F1 score * Few shot * Seq2seq
