  This paper presents a vector initialization approach for the SemEval2020 Task
1: Unsupervised Lexical Semantic Change Detection. Given two corpora belonging
to different time periods and a set of target words, this task requires us to
classify whether a word gained or lost a sense over time (subtask 1) and to
rank them on the basis of the changes in their word senses (subtask 2). The
proposed approach is based on using Vector Initialization method to align GloVe
embeddings. The idea is to consecutively train GloVe embeddings for both
corpora, while using the first model to initialize the second one. This paper
is based on the hypothesis that GloVe embeddings are more suited for the Vector
Initialization method than SGNS embeddings. It presents an intuitive reasoning
behind this hypothesis, and also talks about the impact of various factors and
hyperparameters on the performance of the proposed approach. Our model ranks
13th and 10th among 33 teams in the two subtasks. The implementation has been
shared publicly.
