Evaluation of Five Sentence Similarity Models on Electronic Medical Records
Qingyu Chen, Jingcheng Du, Sun Kim, W. John Wilbur, Zhiyong Lu · 2019
Capturing the semantic similarity between sentences plays a vital role in several primary applications in biomedical and clinical domains: biomedical sentence search, evidence attribution, question-answering and text summarization. In this pilot study, we evaluated the effectiveness of five representative sentence similarity models, ranging from traditional machine learning methods to the latest bidirectional transformers in the clinical domain. The evaluation was performed on a dataset consisting of over 1K sentence pairs from EMRs - the largest public dataset in this domain by far. The results show that embeddings on large biomedical corpora are the most effective methods. It also demonstrates that CNN and BERT are effective to capture sentence similarity under relatively small datasets.