# Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts

arXiv: https://arxiv.org/abs/2210.03797 · HTML: https://mlsanity.com/paper/2210.03797/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 7 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [Mind the Gap: Assessing Temporal Generalization in Neural Language Models](https://mlsanity.com/paper/2102.01951/) (2021)
- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://mlsanity.com/paper/2102.07033/) (2021)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (2019)
- [TimeLMs: Diachronic Language Models from Twitter](https://mlsanity.com/paper/2202.03829/) (2022)
- [TweetNLP: Cutting-Edge Natural Language Processing for Social Media](https://mlsanity.com/paper/2206.14774/) (2022)
- [Temporal Effects on Pre-trained Models for Language Processing Tasks](https://mlsanity.com/paper/2111.12790/) (2021)
- [T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition](https://mlsanity.com/paper/2209.12616/) (2022)
- [Short-Term Meaning Shift: A Distributional Exploration](https://mlsanity.com/paper/1809.03169/) (2018)
- [Twitter Topic Classification](https://mlsanity.com/paper/2209.09824/) (2022)
- [Parsing Tweets into Universal Dependencies](https://mlsanity.com/paper/1804.08228/) (2018)
- [Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis](https://mlsanity.com/paper/2201.07281/) (2022)

## Built on by (most cited first, 28 of 28)

- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://mlsanity.com/paper/2311.08526/) (2023)
- [UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition](https://mlsanity.com/paper/2308.03279/) (2023)
- [Rethinking Negative Instances for Generative Named Entity Recognition](https://mlsanity.com/paper/2402.16602/) (2024)
- [Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023?](https://mlsanity.com/paper/2212.09747/) (2022)
- [SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research](https://mlsanity.com/paper/2310.14757/) (2023)
- [Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data](https://mlsanity.com/paper/2403.12749/) (2024)
- [JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning](https://mlsanity.com/paper/2310.02953/) (2023)
- [Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition](https://mlsanity.com/paper/2406.11192/) (2024)
- [MasonPerplexity at ClimateActivism 2024: Integrating Advanced Ensemble Techniques and Data Augmentation for Climate Activism Stance and Hate Event Identification](https://mlsanity.com/paper/2402.01976/) (2024)
- [RUIE: Retrieval-based Unified Information Extraction using Large Language Model](https://mlsanity.com/paper/2409.11673/) (2024)
- [KnowCoder-X: Boosting Multilingual Information Extraction via Code](https://mlsanity.com/paper/2411.04794/) (2024)
- [Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models](https://mlsanity.com/paper/2506.22813/) (2025)
- [MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles](https://mlsanity.com/paper/2402.01967/) (2024)
- [PICLe: Pseudo-Annotations for In-Context Learning in Low-Resource Named Entity Detection](https://mlsanity.com/paper/2412.11923/) (2024)
- [Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases](https://mlsanity.com/paper/2403.16396/) (2024)
- [A Multi-Agent LLM Framework for Multi-Domain Low-Resource In-Context NER via Knowledge Retrieval, Disambiguation and Reflective Analysis](https://mlsanity.com/paper/2511.19083/) (2025)
- [Model-Agnostic Meta Learning for Class Imbalance Adaptation](https://mlsanity.com/paper/2604.18759/) (2026)
- [Reliable Financial Named Entity Recognition Under Domain Shift: Confidence Estimation and Selective Prediction](https://mlsanity.com/paper/2608.19558/) (2026)
- [Keeping in Time: Adding Temporal Context to Sentiment Analysis Models](https://mlsanity.com/paper/2309.13562/) (2023)
- [On Significance of Subword tokenization for Low Resource and Efficient Named Entity Recognition: A case study in Marathi](https://mlsanity.com/paper/2312.01306/) (2023)
- [A Systematic Analysis on the Temporal Generalization of Language Models in Social Media](https://mlsanity.com/paper/2405.13017/) (2024)
- [Black-box Model Merging for Language-Model-as-a-Service with Massive Model Repositories](https://mlsanity.com/paper/2509.12951/) (2025)
- [A Reasoning Paradigm for Named Entity Recognition](https://mlsanity.com/paper/2511.11978/) (2025)
- [CobwebTM: Probabilistic Concept Formation for Lifelong and Hierarchical Topic Modeling](https://mlsanity.com/paper/2604.14489/) (2026)
- [A Mechanism and Optimization Study on the Impact of Information Density on User-Generated Content Named Entity Recognition](https://mlsanity.com/paper/2604.18944/) (2026)
- [Verb-ICL: Rethinking In-Context Learning for Structured Prediction](https://mlsanity.com/paper/2610.04725/) (2026)

## Abstract (arXiv)

Recent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER). Nonetheless, this progress has been mainly tested in well-formatted documents such as news, Wikipedia, or scientific articles. In social media the landscape is different, in which it adds another layer of complexity due to its noisy and dynamic nature. In this paper, we focus on NER in Twitter, one of the largest social media platforms, and construct a new NER dataset, TweetNER7, which contains seven entity types annotated over 11,382 tweets from September 2019 to August 2021. The dataset was constructed by carefully distributing the tweets over time and taking representative trends as a basis. Along with the dataset, we provide a set of language model baselines and perform an analysis on the language model performance on the task, especially analyzing the impact of different time periods. In particular, we focus on three important temporal aspects in our analysis: short-term degradation of NER models over time, strategies to fine-tune a language model over different periods, and self-labeling as an alternative to lack of recently-labeled data. TweetNER7 is released publicly (https://huggingface.co/datasets/tner/tweetner7) along with the models fine-tuned on it.