ArticleJournal of cheminformatics2018
Chemlistem: chemical named entity recognition using recurrent neural networks.
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 18 papers.
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Who cites it
18 citing papers in PubMed.
- Word embeddings as autonomous predictors in materials design-the effect of inherent variability on information transfer.Journal of cheminformatics · 2026Article
- A machine learning driven automated system to extract multiple information fields from safety data sheet documents.Heliyon · 2025Article
- Accurate disaster entity recognition based on contextual embeddings in self-attentive BiLSTM-CRF.PloS one · 2025Article
- Enhancing chemical synthesis research with NLP: Word embeddings for chemical reagent identification-A case study on nano-FeCu.iScience · 2024Article
- Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition.Journal of proteome research · 2024Article
- Supporting the working life exposome: Annotating occupational exposure for enhanced literature search.PloS one · 2024Article
- Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach.Journal of cheminformatics · 2022Article
- A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles.Database : the journal of biological databases and curation · 2022Article
- Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science.Patterns (New York, N.Y.) · 2022Article
- MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition.Metabolites · 2022Article
- Concept recognition as a machine translation problem.BMC bioinformatics · 2021Article
- Ensemble of Deep Masked Language Models for Effective Named Entity Recognition in Health and Life Science Corpora.Frontiers in research metrics and analytics · 2021Article
- Chemical Entity Recognition for MEDLINE Indexing.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 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
- Extraction of chemical-protein interactions from the literature using neural networks and narrow instance representation.Database : the journal of biological databases and curation · 2019Article
- MER: a shell script and annotation server for minimal named entity recognition and linking.Journal of cheminformatics · 2018Article
- Finding medication doses in the liteature.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Improving the learning of chemical-protein interactions from literature using transfer learning and specialized word embeddings.Database : the journal of biological databases and curation · 2018Article
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) has traditionally been dominated by conditional random fields (CRF)-based approaches but given the success of the artificial neural network techniques known as "deep learning" we decided to examine them as an alternative to CRFs. We present here several chemical named entity recognition systems. The first system translates the traditional CRF-based idioms into a deep learning framework, using rich per-token features and neural word embeddings, and producing a sequence of tags using bidirectional long short term memory (LSTM) networks-a type of recurrent neural net. The second system eschews the rich feature set-and even tokenisation-in favour of character labelling using neural character embeddings and multiple LSTM layers. The third system is an ensemble that combines the results of the first two systems. Our original BioCreative V.5 competition entry was placed in the top group with the highest F scores, and subsequent using transfer learning have achieved a final F score of 90.33% on the test data (precision 91.47%, recall 89.21%).
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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.