Stability of Syntactic Dialect Classification Over Space and Time
This work addresses the need for robust dialect classification in linguistics, but it is incremental as it builds on existing methods for geospatial text classification.
This paper tackled the problem of assessing the stability of dialect classifiers based on syntactic representations over space and time, finding that evaluation of these models can identify syntactic change and internal heterogeneity in dialects.
This paper analyses the degree to which dialect classifiers based on syntactic representations remain stable over space and time. While previous work has shown that the combination of grammar induction and geospatial text classification produces robust dialect models, we do not know what influence both changing grammars and changing populations have on dialect models. This paper constructs a test set for 12 dialects of English that spans three years at monthly intervals with a fixed spatial distribution across 1,120 cities. Syntactic representations are formulated within the usage-based Construction Grammar paradigm (CxG). The decay rate of classification performance for each dialect over time allows us to identify regions undergoing syntactic change. And the distribution of classification accuracy within dialect regions allows us to identify the degree to which the grammar of a dialect is internally heterogeneous. The main contribution of this paper is to show that a rigorous evaluation of dialect classification models can be used to find both variation over space and change over time.