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

Leaman, Robert; Wei, Chih-Hsuan; Lu, Zhiyong

doi:10.1186/1758-2946-7-s1-s3

Cited by 233 publications

(218 citation statements)

References 32 publications

(44 reference statements)

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“…The state-of-the-art system in the BioCreative IV chemical entity mention recognition task is the tmChem system, which uses an ensemble model consisting of two CRF classifiers [Leaman et al, 2015].…”

Section: Bc4chemdmentioning

confidence: 99%

“…Ensemble methods, such as combining two CRF models in the tmChem system [Leaman et al, 2015], show a further boost of performance. Recent studies tried to apply neural network models for automatic feature generation in BioNER.…”

Section: Related Workmentioning

confidence: 99%

“…State-of-the-art BioNER systems often require handcrafted features (e.g., capitalization, prefix and suffix) to be specifically designed for each type of biomedical entities [Sondhi, 2008, Ando, 2007, Leaman et al, 2015, Leaman and Lu, 2016, Zhou and Su, 2004. This feature generation process Sample et al…”

Section: Introductionmentioning

confidence: 99%

“…The model outputs the original sentences labeled with entity types (Figure 1). We compare the proposed multi-task model with state-of-the-art BioNER systems [Sondhi, 2008, Ando, 2007, Leaman et al, 2015, Leaman and Lu, 2016, Zhou and Su, 2004 and neural network NER tools [Crichton et al, 2017, Habibi et al, 2017, Ma and Hovy, 2016, Lample et al, 2016, Liu et al, 2017a on five BioNER benchmark datasets covering four major entity types. Results show that the proposed model achieves substantially better performance than state-of-the-art BioNER systems.…”

Section: Introductionmentioning

confidence: 99%

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Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Wang

Ren

Zhang

et al. 2018

Preprint

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Motivation: Biomedical named entity recognition (BioNER) is the most fundamental task in biomedical text mining. State-of-the-art BioNER systems often require handcrafted features specifically designed for each type of biomedical entities. This feature generation process requires intensive labors from biomedical and linguistic experts, and makes it difficult to adapt these systems to new biomedical entity types. Although recent studies explored using neural network models for BioNER to free experts from manual feature generation, these models still require substantial human efforts to annotate massive training data. Results: We propose a multi-task learning framework for BioNER that is based on neural network models to save human efforts. We build a global model by collectively training multiple models that share parameters, each model capturing the characteristics of a different biomedical entity type. In experiments on five BioNER benchmark datasets covering four major biomedical entity types, our model outperforms state-ofthe-art systems and other neural network models by a large margin, even when only limited training data are available. Further analysis shows that the large performance gains come from sharing character-and word-level information between different biomedical entities. The approach creates new opportunities for text-mining approaches to help biomedical scientists better exploit knowledge in biomedical literature. Availability: The source code for our models is available at https://github.com/yuzhimanhua/lm-lstm-crf, and the corpora are available at https://github.com/cambridgeltl/MTL-Bioinformatics-2016.

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Section: Bc4chemdmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Wang

Ren

Zhang

et al. 2018

Preprint

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“…With such query expansion, we expect to retrieve Leaman et al, 2013;Leaman et al, 2015) for genes/proteins, diseases, and chemicals/drugs. Next, we formulate queries to context patterns and focus on specifically discovering synonymous patterns for chemical-chemical (CC) and chemical-disease (CD) relations.…”

Section: Exploring Query Expansion For Entity Searches In Pubmedmentioning

confidence: 99%

Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis

2016

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an interdisciplinary forum for researchers interested in automated processing of health documents. Health documents encompass electronic health records, clinical guidelines, spontaneous reports for pharmacovigilance, biomedical literature, health forums/blogs or any other type of health-related documents. The LOUHI workshop series fosters interactions between the Computational Linguistics, Medical Informatics and Artificial Intelligence communities. Following the six previous edition of the workshop which were co-located with SMBM 2008 in Turku, Finland, with NAACL 2010 in Los Angeles, California, with Artificial Intelligence in Medicine (AIME 2011) in Bled, Slovenia, during NICTA Techfest 2013 in Sydney, Australia, co-located with EACL 2014 in Gothenburg, Sweden, and with EMNLP 2015 in Lisbon, Portugal, this workshop is co-located this year with EMNLP 2016 in Austin, Texas.The aim of the LOUHI 2016 workshop is to bring together research work on topics related to health documents, particularly emphasizing multidisciplinary aspects of health documentation and the interplay between nursing and medical sciences, information systems, computational linguistics and computer science. The topics include, but are not limited to, the following Natural Language Processing techniques and related areas:• Techniques supporting information extraction, e.g. named entity recognition, negation and uncertainty detection• Classification and text mining applications (e.g. diagnostic classifications such as ICD-10 and nursing intensity scores) and problems (e.g. handling of unbalanced data sets)• Text representation, including dealing with data sparsity and dimensionality issues• Domain adaptation, e.g. adaptation of standard NLP tools (incl. tokenizers, PoS-taggers, etc) to the medical domain• Information fusion, i.e. integrating data from various sources, e.g. structured and narrative documentation• Unsupervised methods, including distributional semantics• Evaluation, gold/reference standard construction and annotation• Syntactic, semantic and pragmatic analysis of health documents• Anonymization/de-identification of health records and ethics• Supporting the development of medical terminologies and ontologies• Individualization of content, consumer health vocabularies, summarization and simplification of text• NLP for supporting documentation and decision making practices iii• Predictive modeling of adverse events, e.g. adverse drug events and hospital acquired infectionsThe call for papers encouraged authors to submit papers describing substantial and completed work but also focus on a contribution, a negative result, a software package or work in progress. We also encouraged to report work on low-resourced languages, addressing the challenges of data sparsity and language characteristic diversity.We received 21 submissions. Each submission went through a double-blind review process which involved three program committee members. Based on comments and rankings supplied by the reviewers, we accepted 13 papers. The overall acceptanc...

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PubChem: A Large‐Scale Public Chemical Database for Drug Discovery

Kim,

Bolton

2023

Methods and Principles in Medicinal Chemistry

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

Cited by 233 publications

References 32 publications

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis

PubChem: A Large‐Scale Public Chemical Database for Drug Discovery

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