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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).
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Named Entity Recognition Models for Machine Learning Interatomic Potentials: A User‐Centric Approach to Knowledge Extraction from Scientific Literature
Machine learning interatomic potential (MLIP) is an emerging technique that has helped achieve molecular dynamics simulations with unprecedented balance between efficiency and accuracy. Recently, the body of MLIP literature has been growing rapidly, which propels the need to automatically process relevant information for researchers to understand and utilize. Named entity recognition (NER), a natural language processing technique that identifies and categorizes information from texts, may help summarize key approaches and findings of relevant papers. In this work, we develop an NER model for MLIP literature by fine‐tuning a pre‐trained language model. To streamline text annotation, we build a user‐friendly web application for annotation and proofreading, which is seamlessly integrated into the training procedure. Our model can identify technical entities with an F1 score of 0.8 for new MLIP paper abstracts using only 60 training paper abstracts and up to 0.75 for scientific texts on different topics. Notably, some “errors” in predictions are actually reasonable decisions, showcasing the model's ability beyond what the performance metrics indicate. This work demonstrates the linguistic capabilities of the NER approach in processing textual information of a specific scientific domain and has the potential to accelerate materials research using language models and contribute to a user‐centric workflow.
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- Award ID(s):
- 2119276
- PAR ID:
- 10599195
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Intelligent Discovery
- ISSN:
- 2943-9981
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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