Word Sense Detection Leveraging Maximum Mean Discrepancy
This addresses word sense change detection for natural language processing, but it is incremental as it applies an existing statistical method to a new task.
The paper tackled the problem of detecting shifts in word meanings over time by proposing MMD-Sense-Analysis, a novel approach using Maximum Mean Discrepancy to select variables and quantify changes, which effectively identifies and explains word sense evolution across historical periods.
Word sense analysis is an essential analysis work for interpreting the linguistic and social backgrounds. The word sense change detection is a task of identifying and interpreting shifts in word meanings over time. This paper proposes MMD-Sense-Analysis, a novel approach that leverages Maximum Mean Discrepancy (MMD) to select semantically meaningful variables and quantify changes across time periods. This method enables both the identification of words undergoing sense shifts and the explanation of their evolution over multiple historical periods. To my knowledge, this is the first application of MMD to word sense change detection. Empirical assessment results demonstrate the effectiveness of the proposed approach.