Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts (2022.aacl-main)
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| Challenge: | Named Entity Recognition (NER) is a longstanding NLP task that consists of identifying an entity in a sentence or document. |
| Approach: | They construct a dataset of seven entity types annotated over 11,382 tweets . they provide a set of language model baselines and analyze the performance of the model . |
| Outcome: | The proposed dataset contains seven entity types annotated over 11,382 tweets . the authors focus on short-term degradation of NER models over time and strategies to fine-tune a language model over different periods . |
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| Challenge: | Social media data such as Twitter messages pose a particular challenge to NLP systems because of their short, noisy nature. |
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| Challenge: | Existing methods to evaluate text data are rarely reported by taking the timestamp of the document into account. |
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Rudra Murthy, Pallab Bhattacharjee, Rahul Sharnagat, Jyotsana Khatri, Diptesh Kanojia, Pushpak Bhattacharyya
| Challenge: | Named Entity Recognition (NER) is a lowerlevel task that aims to provide class labels like Person, Location, Organisation, Time, and Number to words in free text. |
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| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
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| Challenge: | Named Entity Recognition (NER) is a well researched task and widely used in real world NLP scenarios. |
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Hanjun Luo, Yingbin Jin, Yiran Wang, Xinfeng Li, Tong Shang, Xuecheng Liu, Ruizhe Chen, Kun Wang, Hanan Salam, Qingsong Wen, Zuozhu Liu
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T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition (2021.eacl-demos)
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| Challenge: | Language model (LM) pretraining has led to consistent improvements in many downstream tasks, including named entity recognition (NER). |
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