| Challenge: | NER is one of the most important natural language processing tasks. |
| Approach: | They propose to annotate sentences from human-computer interaction, social media, and e-commerce using two rounds of annotation. |
| Outcome: | The proposed system performs the best on all the data sets. |
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UkraiNER: A New Corpus and Annotation Scheme towards Comprehensive Entity Recognition (2024.lrec-main)
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| Challenge: | Named entity recognition excludes nested, discontinuous, non-named entities in practice . despite attempts to broaden their coverage, the most restrictive variant of NER remains the default . |
| Approach: | They propose a new annotation scheme that offers higher comprehensiveness while preserving simplicity. |
| Outcome: | The proposed scheme offers higher comprehensiveness while preserving simplicity . it also includes an annotation tool to implement the scheme on the corpus UkraiNER . |
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 . |
Breaking the Boundaries: A Unified Framework for Chinese Named Entity Recognition Across Text and Speech (2024.findings-emnlp)
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| Challenge: | Existing approaches to Named Entity Recognition (NER) tasks are limited by the complexity of the data and the potential connections between tasks. |
| Approach: | They propose a task to break the boundaries between different modal NER tasks by using a unified data format for inputs from different modalités. |
| Outcome: | The proposed task breaks the boundaries between different modal NER tasks and is a unified implementation of them. |
A Dataset for Named Entity Recognition and Entity Linking in Chinese Historical Newspapers (2024.lrec-main)
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| Challenge: | a novel historical Chinese dataset is used for named entity recognition, entity linking and entity relations. |
| Approach: | They propose a historical Chinese dataset for named entity recognition, entity linking, coreference and entity relations . they use Chinese newspapers from 1872 to 1949 and multilingual bibliographic resources from the same period . |
| Outcome: | The proposed dataset covers different styles and language uses, and is the largest historical Chinese NER dataset with manual annotations from this transitional period. |
Improving Chinese Named Entity Recognition with Multi-grained Words and Part-of-Speech Tags via Joint Modeling (2024.lrec-main)
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| Challenge: | Named entity recognition (CNER) is a fundamental task in natural language processing (NLP). |
| Approach: | They propose a tree parsing approach for jointly modeling Chinese named entity recognition (CNER) with multi-grained word segmentation (MWS) and POS tagging tasks. |
| Outcome: | The proposed approach achieves better or comparable performance with current methods. |
Towards a Unified Multi-Domain Multilingual Named Entity Recognition Model (2023.eacl-main)
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Mayank Kulkarni, Daniel Preotiuc-Pietro, Karthik Radhakrishnan, Genta Indra Winata, Shijie Wu, Lingjue Xie, Shaohua Yang
| Challenge: | Named Entity Recognition is a key task whose performance is sensitive to genre and language. |
| Approach: | They propose a setup for Named Entity Recognition which includes multi-domain and multilingual training and evaluation across 13 domains and 4 languages. |
| Outcome: | The proposed model improves on 13 domains and 4 languages across 13 domain and 4 language domains. |
Named Entity Recognition for Chinese biomedical patents (2020.coling-main)
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| Challenge: | Existing attempts to address NER for Chinese biomedical texts have been limited due to the amount of Chinese biomedicine discoveries being patented. |
| Approach: | They train and evaluate Chinese biomedical patents NER models based on BERT . their model is optimized for Chinese bio-patent data and scored an F1 . |
| Outcome: | The proposed model achieves an F1 score of 0.540.15 for Chinese biomedical patent data. |
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data (2024.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) is a core component of natural language processing, present in a variety of applications such as medical coding, financial news analysis, or legal documents parsing. |
| Approach: | They propose to use Large Language Models (LLMs) to create NuNER, a compact language representation model specialized in the Named Entity Recognition task. |
| Outcome: | The proposed model outperforms similar-sized foundation models in the few-shot regime and is based on a human-annotated dataset. |
BiLSTM-CRF for Persian Named-Entity Recognition ArmanPersoNERCorpus: the First Entity-Annotated Persian Dataset (L18-1)
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| Challenge: | Named-entity recognition (NER) is a natural language processing component that aims to identify all the "named entities" (NEs) in an unstructured text. |
| Approach: | They propose a deep learning approach for name-entity recognition in Persian . they publicize an entity-annotated Persian dataset and train word embeddings . |
| Outcome: | The proposed approach achieves a 77.45% CoNLL F 1 score for Persian NER based on a deep learning architecture and pre-trained word embeddings. |
CNER: Concept and Named Entity Recognition (2024.naacl-long)
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| Challenge: | Concept and Named Entity Recognition (CNER) is a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. |
| Approach: | They propose a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. |
| Outcome: | The proposed task gains +5.4 and +8 macro F1 points when performed as a unified task compared to specialized named entity and concept recognition systems. |