RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection
Ran Iwamoto, Masahiro Yukawa · 2020
This paper describes the model proposed and submitted by our RIJP team to SemEval 2020 Task1: Unsupervised Lexical Semantic Change Detection.In the model, words are represented by Gaussian distributions.For Subtask 1, the model achieved average scores of 0.51 and 0.70 in the evaluation and post-evaluation processes, respectively.The higher score in the post-evaluation process than that in the evaluation process was achieved owing to appropriate parameter tuning.The results indicate that the proposed Gaussian-based embedding model is able to express semantic shifts while having a low computational complexity.