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Article Abstract
Background: Concept 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.
Results: In 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.
Conclusions: We expect that our model can be utilized for effective concept recognition and variety of natural language processing tasks on bioinformatics.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8529713 | PMC |
| http://dx.doi.org/10.1186/s12859-021-04248-8 | DOI Listing |
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