Deep Contextualized Biomedical Abbreviation Expansion

Abstract

Automatic identification and expansion of ambiguous abbreviations are essential for biomedical natural language processing applications, such as information retrieval and question answering systems. In this paper, we present DEep Contextualized Biomedical. Abbreviation Expansion (DECBAE) model. DECBAE automatically collects substantial and relatively clean annotated contexts for 950 ambiguous abbreviations from PubMed abstracts using a simple heuristic. Then it utilizes BioELMo to extract the contextualized features of words, and feed those features to abbreviation-specific bidirectional LSTMs, where the hidden states of the ambiguous abbreviations are used to assign the exact definitions. Our DECBAE model outperforms other baselines by large margins, achieving average accuracy of 0.961 and macro-F1 of 0.917 on the dataset. It also surpasses human performance for expanding a sample abbreviation, and remains robust in imbalanced, low-resources and clinical settings.

Meeting Name

18th BioNLP Workshop and Shared Task (2019: Aug. 1, Florence, Italy)

Department(s)

Engineering Management and Systems Engineering

Second Department

Biological Sciences

Research Center/Lab(s)

Center for High Performance Computing Research

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Association for Computational Linguistics, All rights reserved.

Publication Date

01 Jan 2019

Link to Full Text

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