| Challenge: | Named entity recognition and part of speech tagging require capitalization in training. |
| Approach: | They propose to modify only the casing of the train or test data using lowercasing and truecasing methods to modify the cassing of a model with high performance on both cased and uncased text. |
| Outcome: | The proposed model improves mention detection on noisy out-of-domain Twitter data by 8%. |
Similar Papers
Robustness to Capitalization Errors in Named Entity Recognition (D19-55)
Copied to clipboard
| Challenge: | Existing methods to improve robustness to noise discard given orthographic information, which significantly degrades models' performance on well-formed text. |
| Approach: | They propose a method which allows models to learn to utilize or ignore orthographic information depending on its usefulness in the context. |
| Outcome: | The proposed approach achieves competitive robustness to capitalization errors while making negligible compromises on well-formed text and significantly improving generalization power on noisy user-generated text. |
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 . |
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. |
Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy (2025.coling-main)
Copied to clipboard
| Challenge: | Existing models of named entity recognition (NER) suffer from the problem of Out-of-Entity (OOE), which hinders the achievement of satisfactory performance. |
| Approach: | They propose a framework which fully leverages sentence-level information to improve OOE-NER performance by exploiting pre-trained language models' ability to understand target entity’s sentence context with a template set and refines sentence representation based on positive and negative templates. |
| Outcome: | The proposed framework outperforms state-of-the-art models on five datasets on named entity recognition (NER) tasks. |
Few-Shot Named Entity Recognition: An Empirical Baseline Study (2021.emnlp-main)
Copied to clipboard
Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han
| Challenge: | Existing methods to build named entity recognition systems with limited labeled data are lacking. |
| Approach: | They propose three orthogonal schemes to build named entity recognition systems when labeled data is limited. |
| Outcome: | The proposed NER systems outperform existing methods on few-shot and training-free settings. |
SLENDER: Structured Outputs for SLM-based NER in Low-Resource Englishes (2025.acl-industry)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) for low-resource variants of English remains challenging, as most models are trained on datasets predominantly focused on American or British English. |
| Approach: | They propose a new output format for Named Entity Recognition (NER) that achieves a three-fold reduction in inference time compared to JSON format. |
| Outcome: | The proposed output format achieves a three-fold reduction in inference time on average compared to JSON format, which is widely used for structured outputs. |
T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition (2021.eacl-demos)
Copied to clipboard
| Challenge: | Language model (LM) pretraining has led to consistent improvements in many downstream tasks, including named entity recognition (NER). |
| Approach: | They propose a Python library for NER LM finetuning that facilitates cross-domain and cross-lingual generalization of LMs finetuned on NER. |
| Outcome: | The proposed library outperforms LMs trained on NERs in cross-domain and cross-lingual generalization tests on nine datasets. |
ToNER: Type-oriented Named Entity Recognition with Generative Language Model (2024.lrec-main)
Copied to clipboard
| Challenge: | Input too many potential entity types would distract the model inevitably. |
| Approach: | They propose to use a generative model to exploit entity types' merit on promoting NER task by appending a type matching model to identify the entity types most likely to appear in the sentence. |
| Outcome: | The proposed framework exploits entity types' merit on promoting NER task by adding auxiliary task to the model to discover the entity types. |
CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English (2024.lrec-main)
Copied to clipboard
| Challenge: | a glass ceiling for named entity recognition systems has been suggested for 2021 . however, the performance of the most popular NER benchmarks has plateaued since then . we investigate what NER models are still struggling with . |
| Approach: | They perform a fine-grained evaluation of the model outputs by adding document annotations to the CoNLL-03 English dataset to identify lingering errors. |
| Outcome: | The proposed model is able to correct errors and guide future work. |
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a language understanding task that requires large amounts of in-domain labeled data to perform well. |
| Approach: | They propose a framework which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data. |
| Outcome: | The proposed method brings 10%, 23% and 26% improvements over baselines in few-shot learning, domain transfer and zero-shot settings respectively. |