ArticleBMC bioinformatics2021
Context-aware multi-token concept recognition of biological entities.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Promoting the Integration of Elderly Healthcare and Elderly Nursing: Evidence from the Chinese Government.International journal of environmental research and public health · 2022Article
- Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts.BMC bioinformatics · 2022Article
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2 authors.
Funding
Abstract
backgroundConcept recognition is a term that corresponds to the two sequential steps of named entity recognition and named entity normalization, and plays an essential role in the field of bioinformatics. However, the conventional dictionary-based methods did not sufficiently addressed the variation of the concepts in actual use in literature, resulting in the particularly degraded performances in recognition of multi-token concepts.
resultsIn this paper, we propose a concept recognition method of multi-token biological entities using neural models combined with literature contexts. The key aspect of our method is utilizing the contextual information from the biological knowledge-bases for concept normalization, which is followed by named entity recognition procedure. The model showed improved performances over conventional methods, particularly for multi-token concepts with higher variations.
conclusionsWe expect that our model can be utilized for effective concept recognition and variety of natural language processing tasks on bioinformatics.
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