  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 fastText. For stability measurement, we
investigate the effect of five parameters involved in training these models. We
perform experiments using four real-world datasets from different domains:
Wikipedia, News, Song lyrics, and European parliament proceedings. We also
observe the effect of WEM stability on three downstream tasks: Clustering, POS
tagging, and Fairness evaluation. Our experiments indicate that amongst the
three WEMs, fastText is the most stable, followed by GloVe and Word2Vec.
