# Mitigating Temporal-Drift: A Simple Approach to Keep NER Models Crisp

arXiv: https://arxiv.org/abs/2104.09742 · HTML: https://mlsanity.com/paper/2104.09742/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Variational Adversarial Active Learning](https://mlsanity.com/paper/1904.00370/) (2019)
- [Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change](https://mlsanity.com/paper/1606.02821/) (2016)
- [An empirical study of neural networks for trend detection in time series](https://mlsanity.com/paper/1912.04009/) (2019)
- [USFD: Twitter NER with Drift Compensation and Linked Data](https://mlsanity.com/paper/1511.03088/) (2015)

## Built on by (most cited first, 7 of 7)

- [Temporal Effects on Pre-trained Models for Language Processing Tasks](https://mlsanity.com/paper/2111.12790/) (2021)
- [NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms](https://mlsanity.com/paper/2402.12261/) (2024)
- [TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media](https://mlsanity.com/paper/2209.07216/) (2022)
- [SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research](https://mlsanity.com/paper/2310.14757/) (2023)
- [Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse](https://mlsanity.com/paper/2303.13451/) (2023)
- [Boosting classification reliability of NLP transformer models in the long run](https://mlsanity.com/paper/2302.10016/) (2023)
- [A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts](https://mlsanity.com/paper/2606.27881/) (2026)

## Abstract (arXiv)

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.