A Semi-Supervised Abbreviation Disambiguation Method Based on ACNN and Bi-LSTM - ZHANG Chun-xiang; PANG Shu-yang; GAO Xue-yao (Journal of Harbin University of Science and Technology)
In order to improve disambiguation accuracy of biomedical abbreviations， a semi-supervised abbreviation disambiguation method based on asymmetric convolutional neural networks and bidirectional long short term memory networks is proposed. Abbreviation is viewed as center. Morphology information， part of speech and semantic information from four adjacent lexical units are extracted as disambiguation features. Training corpus is extended by using Xgboost algorithm and LightGBM algorithm， and then expanded training corpus is input into this model. Asymmetric convolutional neural networks (ACNN) and bidirectional long short-term memory (Bi-LSTM) networks are utilized to extract features. Softmax function is applied to semantic classification. MSH corpus is adopted to optimize this model and test its disambiguation performance. Experimental results show that the proposed model can effectively improve disambiguation accuracy of abbreviations by using only a small amount of annotated corpus.