Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types
This work addresses a specific challenge in computational linguistics for researchers analyzing historical language evolution, but it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of classifying types of semantic change in words, such as generalization and specialization, by developing a model that uses synchronic lexical relations and definitions from WordNet, and shows it improves performance on semantic relatedness and change detection tasks.
There is abundant evidence of the fact that the way words change their meaning can be classified in different types of change, highlighting the relationship between the old and new meanings (among which generalization, specialization and co-hyponymy transfer). In this paper, we present a way of detecting these types of change by constructing a model that leverages information both from synchronic lexical relations and definitions of word meanings. Specifically, we use synset definitions and hierarchy information from WordNet and test it on a digitized version of Blank's (1997) dataset of semantic change types. Finally, we show how the sense relationships can improve models for both approximation of human judgments of semantic relatedness as well as binary Lexical Semantic Change Detection.