WkNER: Enhancing Named Entity Recognition with Word Segmentation Constraints and kNN Retrieval (2024.lrec-main)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) tasks require detecting the span and category of the entity from the text block. |
| Approach: | They propose a kNN retrieval enhancement algorithm that incorporates word segmentation information to enhance the model’s generalization ability and alleviate the problem of missing entity tokens in prediction. |
| Outcome: | The proposed method improves the performance of baseline models and achieves better or compared recognition accuracy than previous state-of-the-art models in multiple public Chinese and English datasets. |
Similar Papers
Improving Pre-trained Language Models with Knowledge Enhancement and Filtering Framework (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing knowledge enhancement techniques for pre-trained language models (PLMs) introduce noisy entity representations. |
| Approach: | They propose a knowledge enhancement filter that integrates external knowledge bases to enhance PLMs' ability to capture entity knowledge. |
| Outcome: | The proposed method achieves the highest F1-score and accuracy while reducing the computational cost by 1.7-2.5x. |
NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
Copied to clipboard
| 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 . |
ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) exhibit remarkable adaptability across domains, but they are often not suitable for structured knowledge extraction tasks such as named entity recognition (NER). |
| Approach: | They propose a method that instructs LLMs to self-reflect on the specific domain and generates domain-relevant attributes for creating attribute-rich training data. |
| Outcome: | The proposed method produces NER datasets in domains with domain-relevant attributes and generates entity terms and NER context data around these entities. |
The Utility and Interplay of Gazetteers and Entity Segmentation for Named Entity Recognition in English (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent papers introduce methods to incorporate gazetteer features and entity segmentation techniques in neural named entity recognition models. |
| Approach: | They propose to integrate gazetteer features and entity segmentation techniques into neural named entity recognition models. |
| Outcome: | The proposed methods improve entity segmentation and not just entity typing. |
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition (2025.coling-main)
Copied to clipboard
Yuming Yang, Wantong Zhao, Caishuang Huang, Junjie Ye, Xiao Wang, Huiyuan Zheng, Yang Nan, Yuran Wang, Xueying Xu, Kaixin Huang, Yunke Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Current Large Language Models struggle with complex entity taxonomies in open domains and lack NER capabilities. |
| Approach: | They propose a dataset to guide LLMs' generalization in Open NER under a universal entity taxonomy. |
| Outcome: | The proposed model outperforms GPT-4 in 3 out-of-domain benchmarks across 15 datasets and 6 languages. |
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision (2021.emnlp-main)
Copied to clipboard
Mieradilijiang Maimaiti, Yang Liu, Yuanhang Zheng, Gang Chen, Kaiyu Huang, Ji Zhang, Huanbo Luan, Maosong Sun
| Challenge: | Recent state-of-the-art (SOTA) effective neural network methods have been used in Chinese word segmentation (CWS) However, the robustness of the previous neural methods is limited by the large-scale annotated corpus. |
| Approach: | They propose a self-supervised Chinese word segmentation approach with a straightforward and effective architecture. |
| Outcome: | The proposed approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness. |
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing studies have focused on auto-regressive models for generalization in named entity (NE) typing (NET) and recognition (NER) . however, little has been done in this direction for auto-Regressive LMs despite their popularity and potential to express a wide variety of NLP tasks in the same unified format. |
| Approach: | They propose to probe auto-regressive LMs for NET and NER generalization by resorting to meta-learning to assess the model's memorization of NEs. |
| Outcome: | The proposed model performs well on NET and NER generalization tasks, while relying more on NE than contextual cues in few-shot NER. |
Sentence-Level Resampling for Named Entity Recognition (2022.naacl-main)
Copied to clipboard
| Challenge: | named entity recognition (NER) tasks are often dominated by the majority of non-entity tokens in text . a data imbalance problem is causing the NER models to ignore named entities . |
| Approach: | They propose a set of sentence-level resampling methods to reduce data imbalance . they use a training sentence to compute the importance of each training sentence based on its tokens and entities . |
| Outcome: | The proposed methods outperform sub-sentence-level resampling, data augmentation, and loss functions on multiple corpora. |
GPT-NER: Named Entity Recognition via Large Language Models (2025.findings-naacl)
Copied to clipboard
Shuhe Wang, Xiaofei Sun, Xiaoya Li, Rongbin Ouyang, Fei Wu, Tianwei Zhang, Jiwei Li, Guoyin Wang, Chen Guo
| Challenge: | Large-scale language models (LLMs) have shown impressive ability for in-context learning with limited training data. |
| Approach: | They propose a novel sequence labeling task that transforms a sequence labeled as a text-generation task into a self-verification task that LLMs can adapt to. |
| Outcome: | The proposed model performs better on NER than supervised models on a variety of tasks . the proposed model can be easily adapted by LLMs to generate a text sequence . |
GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) models are crucial for academic writing . existing ground truth datasets do not treat fine-grained types like ML model and model architecture as separate entity types . |
| Approach: | They propose to annotate 100 full-text scientific publications and a first baseline model for 10 entity types centered around ML models and datasets. |
| Outcome: | The proposed model can be used to identify 10 entity types in scientific articles . existing models cannot recognize fine-grained models like ML models and model architecture . |