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This paper presents a vector initialization approach for the SemEval2020 Task1: Unsupervised Lexical Semantic Change Detection. Given two corpora belongingto different time periods and a set of target words, this task requires us toclassify whether a word gained or lost a sense over time (subtask 1) and torank them on the basis of the changes in their word senses (subtask 2). Theproposed approach is based on using Vector Initialization method to align GloVeembeddings. The idea is to consecutively train GloVe embeddings for bothcorpora, while using the first model to initialize the second one. This paperis based on the hypothesis that GloVe embeddings are more suited for the VectorInitialization method than SGNS embeddings. It presents an intuitive reasoningbehind this hypothesis, and also talks about the impact of various factors andhyperparameters on the performance of the proposed approach. Our model ranks13th and 10th among 33 teams in the two subtasks. The implementation has beenshared publicly.

GloVeInit at SemEval-2020 Task 1: Using GloVe Vector Initialization for Unsupervised Lexical Semantic Change Detection