Short-Term Meaning Shift: A Distributional Exploration
This work addresses meaning shift detection for online communities, but it is incremental as it builds on existing models with a small annotated dataset.
The paper tackled the problem of detecting meaning shift over short periods in online communities using distributional representations, finding that a standard model struggles to distinguish meaning shift from referential phenomena, and proposed a measure of contextual variability to address this issue.
We present the first exploration of meaning shift over short periods of time in online communities using distributional representations. We create a small annotated dataset and use it to assess the performance of a standard model for meaning shift detection on short-term meaning shift. We find that the model has problems distinguishing meaning shift from referential phenomena, and propose a measure of contextual variability to remedy this.