A Comparison of Word Embeddings for the Biomedical Natural Language Processing

摘要

BackgroundWord embeddings have been prevalently used in biomedical Natural Language Processing (NLP) applications due to the vector representations of words capturing useful semantic properties and linguistic relationships between words. Different textual resources (e.g., Wikipedia and biomedical literature corpus) have been utilized in biomedical NLP to train word embeddings and these word embeddings have been commonly leveraged as feature input to downstream machine learning models. However, there has been little work on evaluating the word embeddings trained from different textual resources.