ArticleJournal of chemical information and modeling2022
Single Model for Organic and Inorganic Chemical Named Entity Recognition in ChemDataExtractor.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Article
- Autogenerating a Domain-Specific Question-Answering Data Set to Enable High-Performing Language Models for Magnetic Materials.Journal of chemical information and modeling · 2026Article
- Automatic Generation of a Mechanical Properties Question-Answering Data Set for Language Model Benchmarking: A Comparative Study of BERT, XLNet, and LLaMA Models.Journal of chemical information and modeling · 2026Article
- A dataset of Curie and Néel temperatures auto-generated with ChemDataExtractor and the Snowball algorithm.Scientific data · 2025Article
- Cost-Efficient Domain-Adaptive Pretraining of Language Models for Optoelectronics Applications.Journal of chemical information and modeling · 2025Article
- MechBERT: Language Models for Extracting Chemical and Property Relationships about Mechanical Stress and Strain.Journal of chemical information and modeling · 2025Article
- A Database of Stress-Strain Properties Auto-generated from the Scientific Literature using ChemDataExtractor.Scientific data · 2024Article
- Application of machine reading comprehension techniques for named entity recognition in materials science.Journal of cheminformatics · 2024Article
- A document-level information extraction pipeline for layered cathode materials for sodium-ion batteries.Scientific data · 2024Article
- A database of thermally activated delayed fluorescent molecules auto-generated from scientific literature with ChemDataExtractor.Scientific data · 2024Article
- Snowball 2.0: Generic Material Data Parser for ChemDataExtractor.Journal of chemical information and modeling · 2023Article
- Automated Construction of a Photocatalysis Dataset for Water-Splitting Applications.Scientific data · 2023Article
- DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications.Nature communications · 2023Article
- OpticalBERT and OpticalTable-SQA: Text- and Table-Based Language Models for the Optical-Materials Domain.Journal of chemical information and modeling · 2023Article
- BatteryBERT: A Pretrained Language Model for Battery Database Enhancement.Journal of chemical information and modeling · 2022Article
- BatteryDataExtractor: battery-aware text-mining software embedded with BERT models.Chemical science · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemical Named Entity Recognition (NER) forms the basis of information extraction tasks in the chemical domain. However, while such tasks can involve multiple domains of chemistry at the same time, currently available named entity recognizers are specialized in one part of chemistry, resulting in such workflows failing for a biased subset of mentions. This paper presents a single model that performs at close to the state-of-the-art for
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.