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. |
Similar Papers
ZeroNER: Fueling Zero-Shot Named Entity Recognition via Entity Type Descriptions (2025.findings-acl)
Copied to clipboard
Alessio Cocchieri, Marcos Martínez Galindo, Giacomo Frisoni, Gianluca Moro, Claudio Sartori, Giuseppe Tagliavini
| Challenge: | Existing zero-shot learning methods rely on entity type names for generalization . current solutions require large datasets and prioritize a handful of commonly occurring types . |
| Approach: | They propose a description-driven framework that enhances hard zero-shot NER in low-resource settings. |
| Outcome: | The proposed framework outperforms existing models by up to 16% in the F1 score . it also surpasses baseline models that use type names alone . |
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. |
Few-Shot Class-Incremental Learning for Named Entity Recognition (2022.acl-long)
Copied to clipboard
| Challenge: | Existing models of Named Entity Recognition (NER) are trained on large datasets with predefined entity classes, but data of new classes arrives constantly. Existing work on NER relies on the assumption that there exists abundance of labeled data for the training of new class. |
| Approach: | They propose a few-shot class-incremental learning problem where NER model is trained with only few labeled samples of the new classes without forgetting knowledge of the old ones. |
| Outcome: | The proposed model improves over existing baselines by reconstructing training data of old classes and real data from the training set. |
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition (2024.eacl-long)
Copied to clipboard
| Challenge: | Few-shot named entity recognition (NER) uses only a few annotated examples to identify named entities within text. |
| Approach: | They propose to leverage natural language descriptions of each entity type to perform few-shot named entity recognition. |
| Outcome: | The proposed model learns to interpret verbalized descriptions of entities using natural language descriptions of their types and their verbalizations. |
Less than One-shot: Named Entity Recognition via Extremely Weak Supervision (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Named entity recognition (NER) problem is performed under extremely weak supervision . XWS setting is considered weaker than 1-shot since example entity is given in context-free way . |
| Approach: | They propose a method that uses extremely weak supervision to train named entity recognition models. |
| Outcome: | The proposed method outperforms the state-of-the-art few-shot methods with 1-shot supervision and ChatGPT annotations significantly. |
A Novel Three-stage Framework for Few-shot Named Entity Recognition (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for Named Entity Recognition (NER) rely on labeled data, but data scarcity is a major challenge. |
| Approach: | They propose a framework for Few-shot Named Entity Recognition that can learn from limited labeled data and generalize to new domains. |
| Outcome: | The proposed framework surpasses existing methods on several benchmarks. |
Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning (2020.emnlp-main)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is widely adopted in several domains, such as news, medical, and social media. |
| Approach: | They propose a few-shot named entity recognition system based on nearest neighbor learning and structured inference. |
| Outcome: | The proposed method improves F1 scores on standard few-shot NER evaluation tasks by 6% to 16% relative to previous methods. |
DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to named entity recognition (NER) are limited to high-resource languages like English and Chinese. |
| Approach: | They propose a framework to make full use of annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition. |
| Outcome: | The proposed framework makes full use of both annotated source and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER). |
Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification (2021.acl-long)
Copied to clipboard
| Challenge: | Named entity recognition and classification (NERC) tasks require annotated data for the target classes during training. |
| Approach: | They propose a novel approach that leverages the fact that textual descriptions for many entity classes occur naturally. |
| Outcome: | The proposed approach outperforms baselines adapted from machine reading comprehension and zero-shot text classification. |
NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings (2025.findings-emnlp)
Copied to clipboard
| Challenge: | NER Retriever uses a user-defined type description to retrieve documents mentioning entities of that type. |
| Approach: | They propose a zero-shot retrieval framework for ad-hoc Named Entity Recognition . a user-defined type description is used to retrieve documents mentioning entities of that type . |
| Outcome: | The proposed framework outperforms lexical and dense retrieval baselines on three benchmarks. |