ArticleBioMed research international2016
ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Advancing battery research through large language models: A review.Innovation (Cambridge (Mass.)) · 2026Review
- FooDrugs: a comprehensive food-drug interactions database with text documents and transcriptional data.Database : the journal of biological databases and curation · 2023Article
- Entity relation extraction in the medical domain: based on data augmentation.Annals of translational medicine · 2022Article
- Single Model for Organic and Inorganic Chemical Named Entity Recognition in ChemDataExtractor.Journal of chemical information and modeling · 2022Article
- Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types.Journal of translational medicine · 2021Article
- From Tokenization to Self-Supervision: Building a High-Performance Information Extraction System for Chemical Reactions in Patents.Frontiers in research metrics and analytics · 2021Article
- UArizona at the MADE1.0 NLP Challenge.Proceedings of machine learning research · 2018Article
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3 authors.
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Abstract
Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities.
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