Interpretable Word Embeddings for Medical Domain

Word embeddings are finding their increasing application in a variety of biomedical Natural Language Processing (bioNLP) tasks, ranging from drug discovery to automated disease diagnosis. While these word embeddings in their entirety have shown meaningful syntactic and semantic regularities, however, the meaning of individual dimensions remains elusive. This becomes problematic both in general and particularly in sensitive domains such as bio-medicine, wherein, the interpretability of results is crucial to its widespread adoption. To address this issue, in this study, we aim to improve the interpretability of pre-trained word embeddings generated from a text corpora, and in doing so provide a systematic approach to formalize the problem. More specifically, we exploit the rich categorical knowledge present in the biomedical domain, and propose to learn a transformation matrix that transforms the input embeddings to a new space where they are both interpretable and retain their original expressive features. Experiments conducted on the largest available biomedical corpus suggests that the model is capable of performing interpretability that resembles closely to the human-level intuition.