Manifold biomedical text sentence embedding

Pretrained distributed sentence embeddings have been proven to be useful in various biomedical text tasks. However, the current research on biomedical text sentence embeddings is mainly based on Euclidean space. The geometric structure of sentences and the relations with the representations of sentence context contribute to more accurate representations of sentence semantics and still need further investigation. To address this issue, in this study, we propose a manifold biomedical text sentence embedding model. To learn biomedical text sentence embedding in the manifold space, we develop an efficient optimization algorithm with neighbourhood preserving embedding based on manifold optimization. We conducted experiments on two tasks of biomedical text classification and clustering, and the experimental results outperformed the state-of-the-art baseline models.