The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation
This work addresses the challenge of quantifying linguistic shifts for researchers in computational linguistics and NLP, offering an incremental improvement over existing methods with enhanced flexibility and efficiency.
The paper tackles the problem of detecting corpus-level language shifts by introducing the global anchor method, showing it is theoretically and empirically equivalent to the alignment method for detection but superior in applicability, such as handling embeddings of different dimensionalities and enabling fine-grained analysis of language evolution and domain adaptation.
Language is dynamic, constantly evolving and adapting with respect to time, domain or topic. The adaptability of language is an active research area, where researchers discover social, cultural and domain-specific changes in language using distributional tools such as word embeddings. In this paper, we introduce the global anchor method for detecting corpus-level language shifts. We show both theoretically and empirically that the global anchor method is equivalent to the alignment method, a widely-used method for comparing word embeddings, in terms of detecting corpus-level language shifts. Despite their equivalence in terms of detection abilities, we demonstrate that the global anchor method is superior in terms of applicability as it can compare embeddings of different dimensionalities. Furthermore, the global anchor method has implementation and parallelization advantages. We show that the global anchor method reveals fine structures in the evolution of language and domain adaptation. When combined with the graph Laplacian technique, the global anchor method recovers the evolution trajectory and domain clustering of disparate text corpora.