Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction

Qingyun Wang, Zixuan Zhang, Hongxiang Li, Xuan Liu, Jiawei Han, Huimin Zhao, Heng Ji


Abstract
Fine-grained few-shot entity extraction in the chemical domain faces two unique challenges. First, compared with entity extraction tasks in the general domain, sentences from chemical papers usually contain more entities. Moreover, entity extraction models usually have difficulty extracting entities of long-tailed types. In this paper, we propose Chem-FINESE, a novel sequence-to-sequence (seq2seq) based few-shot entity extraction approach, to address these two challenges. Our Chem-FINESE has two components: a seq2seq entity extractor to extract named entities from the input sentence and a seq2seq self-validation module to reconstruct the original input sentence from extracted entities. Inspired by the fact that a good entity extraction system needs to extract entities faithfully, our new self-validation module leverages entity extraction results to reconstruct the original input sentence. Besides, we design a new contrastive loss to reduce excessive copying during the extraction process. Finally, we release ChemNER+, a new fine-grained chemical entity extraction dataset that is annotated by domain experts with the ChemNER schema. Experiments in few-shot settings with both ChemNER+ and CHEMET datasets show that our newly proposed framework has contributed up to 8.26% and 6.84% absolute F1-score gains respectively.
Anthology ID:
2024.findings-eacl.1
Original:
2024.findings-eacl.1v1
Version 2:
2024.findings-eacl.1v2
Volume:
Findings of the Association for Computational Linguistics: EACL 2024
Month:
March
Year:
2024
Address:
St. Julian’s, Malta
Editors:
Yvette Graham, Matthew Purver
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–16
Language:
URL:
https://aclanthology.org/2024.findings-eacl.1
DOI:
Bibkey:
Cite (ACL):
Qingyun Wang, Zixuan Zhang, Hongxiang Li, Xuan Liu, Jiawei Han, Huimin Zhao, and Heng Ji. 2024. Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction. In Findings of the Association for Computational Linguistics: EACL 2024, pages 1–16, St. Julian’s, Malta. Association for Computational Linguistics.
Cite (Informal):
Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction (Wang et al., Findings 2024)
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