Challenge: Named Entity Recognition (NER) is a fundamental task to extract key information from texts, but annotated resources are scarce for dialects.
Approach: They present the first dialectal NER dataset for German, BarNER, with 161K tokens annotated on Bavarian Wikipedia articles and tweets.
Outcome: The proposed dataset improves on bar-wiki and moderately on bartweet with training first on Bavarian .

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A Named Entity Recognition Shootout for German (P18-2)

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Challenge: Named entity recognition and classification (NER) is a central component in many natural language processing pipelines.
Approach: They propose to build a model for German named entity recognition that performs at the state of the art for both contemporary and historical texts.
Outcome: The proposed model outperforms the CRF and BiLSTM on large and small datasets.
MultiCoNER: A Large-scale Multilingual Dataset for Complex Named Entity Recognition (2022.coling-1)

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Challenge: Named Entity Recognition (NER) is a core task in Natural Language Processing.
Approach: They present a large multilingual dataset for Named Entity Recognition that covers 3 domains across 11 languages and multilingual and code-mixing subsets.
Outcome: The proposed dataset is large and multilingual, covering 11 languages and subsets.
People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts (2023.findings-acl)

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Challenge: Pre-trained named entity recognition models are inaccurate on modern corpora due to differences in language OCR errors.
Approach: They develop a named entity recognition (NER) corpus of 3.6M sentences from medieval charters written mainly in Czech, Latin, and German.
Outcome: The proposed model achieves entity-level Precision of 72.81–93.98% with 58.14–81.77% Recall on a manually-annotated test dataset.
MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation) (2022.findings-naacl)

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Challenge: Named Entity Recognition (NER) is a process of identifying named entities in unstructured texts and classifying them through specific semantic categories.
Approach: They propose a method for automatically producing NER annotations and introduce a manually-annotated test set.
Outcome: The proposed method covers 10 languages, 15 NER categories and 2 textual genres and a manually-annotated test set.
NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)

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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 .
Approach: They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition .
Outcome: The proposed model matches or surpasses existing models in NER tasks . the proposed model is based on a large, silver-annotated corpus of 4 million paragraphs .
Fine-grained Named Entity Annotations for German Biographic Interviews (2020.lrec-1)

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Challenge: a NER annotation scheme is adapted for a corpus of transcripts of biographic interviews with emigrants to German . a dataset of spoken data and teaser tweets from newspaper sites are used to test the NER inventory.
Approach: They propose a fine-grained NER annotation scheme with 30 labels and apply it to German data.
Outcome: The proposed NER annotations can be applied to spoken data and teaser tweets from newspaper sites and achieve good inter-annotator agreement.
OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages (2025.emnlp-main)

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Challenge: Existing datasets are not consistently formatted and use a variety of chunk encodings (IOB, BIO, etc.), often without documentation.
Approach: They present OpenNER 1.0, a standardized collection of openly-available named entity recognition (NER) datasets.
Outcome: The proposed datasets correct annotation format issues and provide a structure that enables research in multilingual and multi-ontology NER.
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 .
DaN+: Danish Nested Named Entities and Lexical Normalization (2020.coling-main)

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Challenge: Named Entity Recognition (NER) is a task of finding entities in text, such as locations, organizations, and persons.
Approach: They propose a multi-domain corpus and annotation guidelines for Danish nested named entities and lexical normalization to support research on cross-lingual cross-domain learning for a less-resourced language.
Outcome: The proposed model outperforms existing models on the Danish Named Entity Recognition task and shows that it is robust to domain shifts and is highly effective on the least canonical data.
Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis (2022.lrec-1)

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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.
Approach: They create a Twitter-based NER corpus and train Tweet NLP models on it . they annotate named entities in TB2 using Amazon Mechanical Turk .
Outcome: The proposed model outperforms existing models on Twitter and other social media platforms.

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