Disambiguation of Biomedical Acronyms Based on a Bidirectional Recurrent Neural Network of Character-level Features

Abstract

Polysemic acronyms are very common in the field of biomedicine. These acronyms have different senses in different contexts. The ambiguity of acronyms may cause significant negative impact on the understanding of the full text by machine learning. To address the disambiguation of acronyms in the biomedical domain, most associated studies are based on methods using word-level contextual features. These methods require abundant relevant external resources for model training, and the accuracy of their disambiguation of acronyms may decrease greatly upon the lack of external resources. In this study, disambiguation of biomedical acronyms was investigated on the basis of the character-level feature model to realize the disambiguation of biomedical acronyms with largely limited external corpora. First, sentences containing ambiguous acronyms were extracted through retrieval and the feature vector of the context were initialized by using the character-level features. Second, these initial vectors were input into the bidirectional long shortterm memory neutral network model for training. Finally, the disambiguation of acronyms was realized by the outputs of the neutral network model through the Softmax classification approach. The results of acronym disambiguation based on character-level feature model were also compared with those based on word-level feature models. Results demonstrate that the average accuracy of the character-level feature neutral network algorithm reaches 85.82% on the dataset of 106 common biomedical acronyms. Thus, the character-level feature neutral network algorithm is superior to the traditional methods, which use a large number of external resources. This study confirms that the disambiguation method based on character-level features is applicable to the disambiguation of biomedical acronyms under limited relevant data.