ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision

Xuan Wang, Vivian Hu, Xiangchen Song, Shweta Garg, Jinfeng Xiao, Jiawei Han


Abstract
Scientific literature analysis needs fine-grained named entity recognition (NER) to provide a wide range of information for scientific discovery. For example, chemistry research needs to study dozens to hundreds of distinct, fine-grained entity types, making consistent and accurate annotation difficult even for crowds of domain experts. On the other hand, domain-specific ontologies and knowledge bases (KBs) can be easily accessed, constructed, or integrated, which makes distant supervision realistic for fine-grained chemistry NER. In distant supervision, training labels are generated by matching mentions in a document with the concepts in the knowledge bases (KBs). However, this kind of KB-matching suffers from two major challenges: incomplete annotation and noisy annotation. We propose ChemNER, an ontology-guided, distantly-supervised method for fine-grained chemistry NER to tackle these challenges. It leverages the chemistry type ontology structure to generate distant labels with novel methods of flexible KB-matching and ontology-guided multi-type disambiguation. It significantly improves the distant label generation for the subsequent sequence labeling model training. We also provide an expert-labeled, chemistry NER dataset with 62 fine-grained chemistry types (e.g., chemical compounds and chemical reactions). Experimental results show that ChemNER is highly effective, outperforming substantially the state-of-the-art NER methods (with .25 absolute F1 score improvement).
Anthology ID:
2021.emnlp-main.424
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5227–5240
Language:
URL:
https://aclanthology.org/2021.emnlp-main.424
DOI:
10.18653/v1/2021.emnlp-main.424
Bibkey:
Cite (ACL):
Xuan Wang, Vivian Hu, Xiangchen Song, Shweta Garg, Jinfeng Xiao, and Jiawei Han. 2021. ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5227–5240, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision (Wang et al., EMNLP 2021)
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PDF:
https://aclanthology.org/2021.emnlp-main.424.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.424.mp4