Papers with Named-entity recognition
BERTweet: A pre-trained language model for English Tweets (2020.emnlp-demos)
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| Challenge: | Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base on three Tweet NLP tasks: Part-of-speech tagging, Named-entity recognition and text classification. |
| Approach: | They propose to train a pre-trained language model for English Tweets using the RoBERTa pre training procedure and use it to train the model. |
| Outcome: | Experiments show that the model outperforms baseline models on three Tweet NLP tasks: Part-of-speech tagging, Named-entity recognition and text classification. |
KIND: an Italian Multi-Domain Dataset for Named Entity Recognition (2022.lrec-1)
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| Challenge: | Named-entity recognition is a task that uses named entities to classify texts . annotated data are time and money consuming, since they need to be created by experts of the domain of the annotation that is going to be done . |
| Approach: | They present an Italian dataset for Named-entity recognition with manual annotations and a semi-automatically annotated part. |
| Outcome: | The proposed dataset covers different styles and language uses, and is the largest in Italy. |
ViDeBERTa: A powerful pre-trained language model for Vietnamese (2023.findings-eacl)
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| Challenge: | Existing models for Vietnamese that perform well on downstream tasks, such as Question answering, are based on Transformer. |
| Approach: | They propose a pre-trained monolingual Vietnamese model with three versions . they fine-tune and evaluate the model on three important natural language downstream tasks, Part-of-speech tagging, Named-entity recognition, and Question answering. |
| Outcome: | The proposed model outperforms the existing model on three important natural language downstream tasks, Part-of-speech tagging, Named-entity recognition, and Question answering. |
PhoBERT: Pre-trained language models for Vietnamese (2020.findings-emnlp)
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| Challenge: | Experimental results show that PhoBERT outperforms the recent best pre-trained multilingual model XLM-R in multiple Vietnamese-specific NLP tasks. |
| Approach: | They present PhoBERT with two versions, Phobert-base and PhoBRET-large, which are pre-trained for Vietnamese. |
| Outcome: | The proposed model outperforms the best pre-trained model XLM-R and improves the state-of-the-art in multiple Vietnamese-specific NLP tasks including Part-of speech tagging, Dependency parsing, Named-entity recognition and Natural language inference. |
Few-shot domain adaptation for named-entity recognition via joint constrained k-means and subspace selection (2025.coling-main)
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| Challenge: | Named-entity recognition (NER) requires large annotated datasets, which limits its applicability across domains with varying entity definitions. |
| Approach: | They propose a weakly-supervised algorithm that combines small labeled datasets with large amounts of unlabeled data. |
| Outcome: | The proposed approach achieves state-of-the-art results in few-shot NER . it combines label supervision, cluster size constraints, and domain-specific discriminative subspace selection. |
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. |