The quest for better clinical word vectors: Ontology based and lexical vector augmentation versus clinical contextual embeddings.
Journal:
Computers in biology and medicine
Published Date:
Apr 28, 2021
Abstract
BACKGROUND: Word vectors or word embeddings are n-dimensional representations of words and form the backbone of Natural Language Processing of textual data. This research experiments with algorithms that augment word vectors with lexical constraints that are popular in NLP research and clinical domain constraints derived from the Unified Medical Language System (UMLS). It also compares the performance of the augmented vectors with Bio + Clinical BERT vectors which have been trained and fine-tuned on clinical datasets.