ArticleDatabase : the journal of biological databases and curation2016
Chemical entity recognition in patents by combining dictionary-based and statistical approaches.
Article in Database : the journal of biological databases and curation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Learning adaptive representations for entity recognition in the biomedical domain.Journal of biomedical semantics · 2021Article
- ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents.Frontiers in research metrics and analytics · 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
- OGER++: hybrid multi-type entity recognition.Journal of cheminformatics · 2019Article
- Automatic identification of relevant chemical compounds from patents.Database : the journal of biological databases and curation · 2019Article
- Entity recognition in the biomedical domain using a hybrid approach.Journal of biomedical semantics · 2017Article
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8 authors.
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Abstract
We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small.Database URL: http://biosemantics.org/chemdner-patents.
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