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.

本文研究了分布式文本表示的应用，比较了基于单词向量和基于上下文的句子向量两种表示方式在不同分类问题下的效果，结果显示，基于 ELMo，Universal Sentence Encoder，Neural-Net Language Model 和 FLAIR 的上下文表示效果较好，相较于基于词向量的表示，分类准确率提高了 2-4%。