ArticleDatabase : the journal of biological databases and curation2016
NERChem: adapting NERBio to chemical patents via full-token features and named entity feature with chemical sub-class composition.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- Knowledge-enhanced biomedical named entity recognition and normalization: application to proteins and genes.BMC bioinformatics · 2020Article
- Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study.JMIR medical informatics · 2019Article
- The extraction of complex relationships and their conversion to biological expression language (BEL) overview of the BioCreative VI (2017) BEL track.Database : the journal of biological databases and curation · 2019Article
- Statistical principle-based approach for gene and protein related object recognition.Journal of cheminformatics · 2018Article
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Authors and funding
3 authors.
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Abstract
Chemical patents contain detailed information on novel chemical compounds that is valuable to the chemical and pharmaceutical industries. In this paper, we introduce a system, NERChem that can recognize chemical named entity mentions in chemical patents. NERChem is based on the conditional random fields model (CRF). Our approach incorporates ( 1 ) class composition, which is used for combining chemical classes whose naming conventions are similar; ( 2 ) BioNE features, which are used for distinguishing chemical mentions from other biomedical NE mentions in the patents; and ( 3 ) full-token word features, which are used to resolve the tokenization granularity problem. We evaluated our approach on the BioCreative V CHEMDNER-patent corpus, and achieved an F-score of 87.17% in the Chemical Entity Mention in Patents (CEMP) task and a sensitivity of 98.58% in the Chemical Passage Detection (CPD) task, ranking alongside the top systems. Database URL: Our NERChem web-based system is publicly available at iisrserv.csie.n cu.edu.tw/nerchem.
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Registered trials
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