ArticleJournal of cheminformatics2018
Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules.
Article in Journal of cheminformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Fourier Kolmogorov-Arnold Network integrated into BioBERT-based model for Biomedical Named Entity Recognition.NPJ digital medicine · 2026Article
- Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach.Journal of cheminformatics · 2022Article
- Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature.Scientific data · 2022Article
- Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science.Patterns (New York, N.Y.) · 2022Article
- Single Model for Organic and Inorganic Chemical Named Entity Recognition in ChemDataExtractor.Journal of chemical information and modeling · 2022Article
- Exploring the effects of drug, disease, and protein dependencies on biomedical named entity recognition: A comparative analysis.Frontiers in pharmacology · 2022Article
- Review
- Automated extraction of chemical synthesis actions from experimental procedures.Nature communications · 2020Article
- Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies.Frontiers in genetics · 2020Article
- Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach.International journal of environmental research and public health · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Chemical named entity recognition (NER) is an active field of research in biomedical natural language processing. To facilitate the development of new and superior chemical NER systems, BioCreative released the CHEMDNER corpus, an extensive dataset of diverse manually annotated chemical entities. Most of the systems trained on the corpus rely on complicated hand-crafted rules or curated databases for data preprocessing, feature extraction and output post-processing, though modern machine learning algorithms, such as deep neural networks, can automatically design the rules with little to none human intervention. Here we explored this approach by experimenting with various deep learning architectures for targeted tokenisation and named entity recognition. Our final model, based on a combination of convolutional and stateful recurrent neural networks with attention-like loops and hybrid word- and character-level embeddings, reaches near human-level performance on the testing dataset with no manually asserted rules. To make our model easily accessible for standalone use and integration in third-party software, we've developed a Python package with a minimalistic user interface.
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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.