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%。

医疗信息学分类问题中基于单词和上下文表示的比较

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

The Rational Speech Acts (RSA) model treats language use as a recursive
process in which probabilistic speaker and listener agents reason about each
other's intentions to enrich the literal semantics of their language along
broadly Gricean lines. RSA has been shown to capture many kinds of
conversational implicature, but it has been criticized as an unrealistic model
of speakers, and it has so far required the manual specification of a semantic
lexicon, preventing its use in natural language processing applications that
learn lexical knowledge from data. We address these concerns by showing how to
define and optimize a trained statistical classifier that uses the intermediate
agents of RSA as hidden layers of representation forming a non-linear
activation function. This treatment opens up new application domains and new
possibilities for learning effectively from data. We validate the model on a
referential expression generation task, showing that the best performance is
achieved by incorporating features approximating well-established insights
about natural language generation into RSA.

通过定义和优化经训练的统计分类器，RSA 模型可用作自然语言处理应用程序的隐藏层，从而打开了新的应用领域和有效从数据学习的可能性，我们在指代表达生成任务中验证了该模型，表明将有关自然语言生成的洞见特征纳入 RSA 中可实现最佳性能。