Extensive evaluation on a large number of word embedding models for language
processing applications is conducted in this work. First, we introduce popular
word embedding models and discuss desired properties of word models and
evaluation methods (or evaluators). Then, we categorize evaluators into
intrinsic and extrinsic two types. Intrinsic evaluators test the quality of a
representation independent of specific natural language processing tasks while
extrinsic evaluators use word embeddings as input features to a downstream task
and measure changes in performance metrics specific to that task. We report
experimental results of intrinsic and extrinsic evaluators on six word
embedding models. It is shown that different evaluators focus on different
aspects of word models, and some are more correlated with natural language
processing tasks. Finally, we adopt correlation analysis to study performance
consistency of extrinsic and intrinsic evalutors.

本文对语言处理应用中大量的词嵌入模型进行了广泛的评估。作者首先介绍了流行的词嵌入模型并讨论了所需的属性以及评估方法，并将评估者分为内在和外在两种类型。作者在六个词嵌入模型上进行了内在和外在检验实验，并做出说明，最后采用相关性分析研究了内在和外在评估者的性能一致性。