Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Ilia Korvigo, Maxim Holmatov, Anatolii Zaikovskii, Mikhail Skoblov

ABSTRACT

Chemical named entity recognition (NER) is an active field of research in biomedical natural language processing. To facilitate the development of new and superior chemical NER systems, BioCreative released the CHEMDNER corpus, an extensive dataset of diverse manually annotated chemical entities. Most of the systems trained on the corpus rely on complicated hand-crafted rules or curated databases for data preprocessing, feature extraction and output post-processing, though modern machine learning algorithms, such as deep neural networks, can automatically design the rules with little to none human intervention. Here we explored this approach by experimenting with various deep learning architectures for targeted tokenisation and named entity recognition. Our final model, based on a combination of convolutional and stateful recurrent neural networks with attention-like loops and hybrid word- and character-level embeddings, reaches near human-level performance on the testing dataset with no manually asserted rules. To make our model easily accessible for standalone use and integration in third-party software, we've developed a Python package with a minimalistic user interface. More... »

PAGES

28

References to SciGraph publications

  • 1999. Text Chunking Using Transformation-Based Learning in NATURAL LANGUAGE PROCESSING USING VERY LARGE CORPORA
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  • 2015-12. The CHEMDNER corpus of chemicals and drugs and its annotation principles in JOURNAL OF CHEMINFORMATICS
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  • 2015-12. CHEMDNER system with mixed conditional random fields and multi-scale word clustering in JOURNAL OF CHEMINFORMATICS
  • 2015-12. Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations in JOURNAL OF CHEMINFORMATICS
  • 2015-12. A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature in JOURNAL OF CHEMINFORMATICS
  • 2015-12. A CRF-based system for recognizing chemical entity mentions (CEMs) in biomedical literature in JOURNAL OF CHEMINFORMATICS
  • 2015-12. Recognition of chemical entities: combining dictionary-based and grammar-based approaches in JOURNAL OF CHEMINFORMATICS
  • 2015-12. tmChem: a high performance approach for chemical named entity recognition and normalization in JOURNAL OF CHEMINFORMATICS
  • 2015-12. Chemical entity extraction using CRF and an ensemble of extractors in JOURNAL OF CHEMINFORMATICS
  • 2015-12. LeadMine: a grammar and dictionary driven approach to entity recognition in JOURNAL OF CHEMINFORMATICS
  • 2015-12. Enhancing of chemical compound and drug name recognition using representative tag scheme and fine-grained tokenization in JOURNAL OF CHEMINFORMATICS
  • 2015-12. A document processing pipeline for annotating chemical entities in scientific documents in JOURNAL OF CHEMINFORMATICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13321-018-0280-0

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    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29796778


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