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Are Word Embedding Methods Stable and Should We Care About It?
[article]
2021
arXiv
pre-print
A representation learning method is considered stable if it consistently generates similar representation of the given data across multiple runs. Word Embedding Methods (WEMs) are a class of representation learning methods that generate dense vector representation for each word in the given text data. The central idea of this paper is to explore the stability measurement of WEMs using intrinsic evaluation based on word similarity. We experiment with three popular WEMs: Word2Vec, GloVe, and
arXiv:2104.08433v1
fatcat:hve3z426nbbfhmpyqxr6beltem