Article ID Journal Published Year Pages File Type
9952377 Journal of Visual Languages & Computing 2018 26 Pages PDF
Abstract
Word embeddings are distributed representations for natural language words, and have been wildly used in many natural language processing tasks. The word embedding space contains local clusters with semantically similar words and meaningful directions, such as the analogy. However, there are different training algorithms and text corpora, which both have a different impact on the generated word embeddings. In this paper, we propose a visual analytics system to visually explore and compare word embeddings trained by different algorithms and corpora. The word embedding spaces are compared from three aspects, i.e., local clusters, semantic directions and diachronic changes, to understand the similarity and differences between word embeddings.
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Physical Sciences and Engineering Computer Science Computer Science Applications
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