Chemical named entity recognition (ChemNER) is a preliminary step in chemical information extraction pipelines. ChemNER has been approached using rule-based, dictionary-based, and feature-engineered based machine learning, and more recently also deep learning based methods. Traditional word-embeddings, ...
As the number of published scientific papers grows everyday, there is also an increasing necessity for automated named entity recognition (NER) systems capable of identifying relevant entities mentioned in a given text, such as chemical entities. Since high precision values are crucial to deliver ...
The NLM-Chem corpus is a rich corpus created for chemical named entity recognition. We compared the NLM-Chem corpus with both the BC5CDR and ChemDNER corpora. Figure5shows a comparison with BC5CDR, because we can compare this both on the mention and ID level. The BC5CDR corpus contains ...
We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming any of the individual systems that we considered. The system is able to provide structure information for most of the compounds that are found. Improved tokeni...
Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings 引入了ELMO embedding ELMo (Peters et al., 2018) and BERT (Devlin et al., 2019) can be used to generate contextualized word representations by combining internal states of different layers in neural langu......
Task 1 addresses chemical named entity recognition, the identification of chemical compounds and their specific roles in chemical reactions. Task 2 focuses on event extraction, the identification of reaction steps, relating the chemical compounds involved in a chemical reaction. Herein, we describe the...
Corbett P, Copestake A: Cascaded classifiers for confidence-based chemical named entity recognition. BMC Bioinformatics. 2008, 9 (Suppl 11): S4-10.1186/1471-2105-9-S11-S4. Article Google Scholar The Penn Treebank Project. last accessed: 28/11/10, [http://www.cis.upenn.edu/~treebank/]...
Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions.We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming any of the individual systems that we considered. The ...
Batchelor CR, Corbett PT: Semantic enrichment of journal articles using chemical named entity recognition. Proceedings of the ACL 2007 Demo and Poster Sessions. 2007, Association for Computational Linguistics Stroudsburg, PA, USA, 45-48. Google Scholar ...
and dictionaries, machine-learning, or combinations thereof9. For instance, many named entity recognition methods have been applied to the detection of chemical entities (compound names and formulas) in text (see, for instance, refs.11,12,13,14,15, as well as ref.9for an extensive review)....