1 research outputs found
Evaluating Word Embedding Models: Methods and Experimental Results
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.Comment: 13 page