  Performance of neural models for named entity recognition degrades over time,
becoming stale. This degradation is due to temporal drift, the change in our
target variables' statistical properties over time. This issue is especially
problematic for social media data, where topics change rapidly. In order to
mitigate the problem, data annotation and retraining of models is common.
Despite its usefulness, this process is expensive and time-consuming, which
motivates new research on efficient model updating. In this paper, we propose
an intuitive approach to measure the potential trendiness of tweets and use
this metric to select the most informative instances to use for training. We
conduct experiments on three state-of-the-art models on the Temporal Twitter
Dataset. Our approach shows larger increases in prediction accuracy with less
training data than the alternatives, making it an attractive, practical
solution.
