tmChem: a high performance approach for chemical named entity recognition and normalization

By Robert Leaman, Chih-Hsuan Wei, Zhiyong Lu. Journal of Cheminformatics, 2015

300 citations recorded by the source corpus.

Chemical compounds and drugs are an important class of entities in biomedical research with great potential in a wide range of applications, including clinical medicine. Locating chemical named entities in the literature is a useful step in chemical text mining pipelines for identifying the chemical mentions, their properties, and their relationships as discussed in the literature.We introduce the tmChem system, a chemical named entity recognizer created by combining two independent machine learning models in an ensemble. We use the corpus released as part of the recent CHEMDNER task to develop and evaluate tmChem, achieving a micro-averaged f-measure of 0.8739 on the CEM subtask (mention-level evaluation) and 0.8745 f-measure on the CDI subtask (abstract-level evaluation). We also report a high-recall combination (0.9212 for CEM and 0.9224 for CDI). tmChem achieved the highest f-measure reported in the CHEMDNER task for the CEM subtask, and the high recall variant achieved the highest recall on both the CEM and CDI tasks.We report that tmChem is a state-of-the-art tool for chemical named entity recognition and that performance for chemical named entity recognition has now tied (or exceeded) the performance previously reported for genes and diseases. Future research should focus on tighter integration between the named entity recognition and normalization steps for improved performance.The source code and a trained model for both models of tmChem is available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmChem. The results of running tmChem (Model 2) on PubMed are available in PubTator: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e882a91435d6a2f0e538b35ac32a786f1981e205

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Citations in the corpus

BioCreative V CDR task corpus: a resource for chemical disease relation extraction (2016, methodology): “Here, we used MeSH identifiers to normalize the chemical mentions because CID relations were previously annotated already in MeSH which is designed for literature indexing and has been used in similar annotation projects (26,30).”
CollaboNet: collaboration of deep neural networks for biomedical named entity recognition (2018, methodology): “Datasets We used 5 datasets (BC2GM [33], BC4CHEMD [34], BC5CDR [35–38], JNLPBA [22], NCBI [21]), all of which were collected by Crichton et al [24] (Table 1).”
Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries (2015, background): “Many systems were proposed for the chemical entity mention recognition subtask and the top-ranked systems are also based on machine learning algorithms [23–27].”
Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task (2016, methodology): “The chemical and disease entities were automatically recognized using NCBI’s in-house tools, DNorm (16) and tmChem (28), respectively.”
Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets (2018, background): “In BioNLP, many highest performing systems still use engineered features fed into a CRF (Wei et al., 2015; Leaman et al., 2015; Leaman and Lu, 2016).”
A survey of current trends in computational drug repositioning (2016, background): “The biomedical and pharmaceutical knowledge available in literature [70, 71] or databases contains vast amount of information for drugs and diseases, which can be automatically mined [72–74] and retrieved [75], thanks to the recent advances in text mining research [76].”
Improving chemical disease relation extraction with rich features and weakly labeled data (2016, methodology): “The gold mentions are provided in the BC5 test set, and the text-mined mentions were computed via tmChem [27] and DNorm [26] for chemicals and diseases respectively.”
Chemical-induced disease relation extraction via convolutional neural network (2017, background): “However, many tasks such as identifying biomedical concepts (10, 11) and extracting relations between biomedical entities (12), still remain challenging.”