GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering
This addresses the problem of automatically detecting when words gain or lose senses over time for computational linguistics, though it appears incremental as it builds on existing embedding methods.
The paper tackled unsupervised lexical semantic change detection by proposing a Gaussian Mixture Models-based algorithm to cluster word similarities across time periods, finding that combining it with Temporal Referencing yielded their best system performance.
This paper describes the system proposed for the SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection. We focused our approach on the detection problem. Given the semantics of words captured by temporal word embeddings in different time periods, we investigate the use of unsupervised methods to detect when the target word has gained or loosed senses. To this end, we defined a new algorithm based on Gaussian Mixture Models to cluster the target similarities computed over the two periods. We compared the proposed approach with a number of similarity-based thresholds. We found that, although the performance of the detection methods varies across the word embedding algorithms, the combination of Gaussian Mixture with Temporal Referencing resulted in our best system.