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

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