UWB @ DIACR-Ita: Lexical Semantic Change Detection with CCA and Orthogonal Transformation
This work addresses the problem of detecting how word meanings evolve over time for computational linguists and historical linguists, providing a competitive, unsupervised, and language-independent approach.
This paper describes a method for detecting lexical semantic change in Italian corpora from different time periods, achieving the 1st rank in the DIACR-Ita shared task. The method involves creating semantic vector spaces for each corpus, computing a linear transformation between them using CCA and Orthogonal Transformation, and then measuring cosine similarities between the transformed vectors.
In this paper, we describe our method for detection of lexical semantic change (i.e., word sense changes over time) for the DIACR-Ita shared task, where we ranked $1^{st}$. We examine semantic differences between specific words in two Italian corpora, chosen from different time periods. Our method is fully unsupervised and language independent. It consists of preparing a semantic vector space for each corpus, earlier and later. Then we compute a linear transformation between earlier and later spaces, using CCA and Orthogonal Transformation. Finally, we measure the cosines between the transformed vectors.