# A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics

arXiv: https://arxiv.org/abs/1906.05468 · HTML: https://mlsanity.com/paper/1906.05468/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Model Stability with Continuous Data Updates](https://mlsanity.com/paper/2201.05692/) (2022)

## Abstract (arXiv)

Distributed representations of text can be used as features when training a statistical classifier. These representations may be created as a composition of word vectors or as context-based sentence vectors. We compare the two kinds of representations (word versus context) for three classification problems: influenza infection classification, drug usage classification and personal health mention classification. For statistical classifiers trained for each of these problems, context-based representations based on ELMo, Universal Sentence Encoder, Neural-Net Language Model and FLAIR are better than Word2Vec, GloVe and the two adapted using the MESH ontology. There is an improvement of 2-4% in the accuracy when these context-based representations are used instead of word-based representations.