Detecting and assessing contextual change in diachronic text documents using context volatility
This work addresses the need for better tools in computational linguistics to analyze semantic shifts in historical texts, though it is incremental as it builds on existing co-occurrence methods.
The authors tackled the problem of measuring semantic dynamics in diachronic text corpora by proposing a new measure called context volatility, which models how terms change context over time, and they evaluated it on synthetic and real newspaper texts, showing it captures changes not addressed by existing methods.
Terms in diachronic text corpora may exhibit a high degree of semantic dynamics that is only partially captured by the common notion of semantic change. The new measure of context volatility that we propose models the degree by which terms change context in a text collection over time. The computation of context volatility for a word relies on the significance-values of its co-occurrent terms and the corresponding co-occurrence ranks in sequential time spans. We define a baseline and present an efficient computational approach in order to overcome problems related to computational issues in the data structure. Results are evaluated both, on synthetic documents that are used to simulate contextual changes, and a real example based on British newspaper texts.