Domain specific word embeddings for natural language processing in radiology.

ABSTRACT:

Background

There has been increasing interest in machine learning based natural language processing (NLP) methods in radiology; however, models have often used word embeddings trained on general web corpora due to lack of a radiology-specific corpus.

Purpose

We examined the potential of Radiopaedia to serve as a general radiology corpus to produce radiology specific word embeddings that could be used to enhance performance on a NLP task on radiological text.

Materials and methods

Embeddings of dimension 50, 100, 200, and 300 were trained on articles collected from Radiopaedia using a GloVe algorithm and evaluated on analogy completion. A shallow neural network using input from either our trained embeddings or pre-trained Wikipedia 2014 + Gigaword 5 (WG) embeddings was

SUBMITTER: Chen TL 

PROVIDER: S-EPMC7856086 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature