Diachronic word embeddings and semantic shifts: a survey
This survey organizes and clarifies research for NLP practitioners working on temporal semantic analysis, but it is incremental as it reviews existing work without introducing new methods.
The paper surveys the current state of research on diachronic word embeddings and semantic shift detection, addressing the lack of cohesion and shared practices in this emerging NLP subfield by comparing methods and outlining challenges and prospects.
Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models. However, this vein of research lacks the cohesion, common terminology and shared practices of more established areas of natural language processing. In this paper, we survey the current state of academic research related to diachronic word embeddings and semantic shifts detection. We start with discussing the notion of semantic shifts, and then continue with an overview of the existing methods for tracing such time-related shifts with word embedding models. We propose several axes along which these methods can be compared, and outline the main challenges before this emerging subfield of NLP, as well as prospects and possible applications.