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A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics by Joshi, Aditya; Karimi, Sarvnaz; Sparks, Ross; Paris, Cecile; MacIntyre, C Raina is a scholarly article available to read on EtoBox.

What is A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics about?

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.

Author

Joshi, Aditya; Karimi, Sarvnaz; Sparks, Ross; Paris, Cecile; MacIntyre, C Raina

Published

2019

Language

EN