Few-shot classification in Named Entity Recognition Task

Abstract

For many natural language processing (NLP) tasks the amount of annotated data is limited. This urges a need to apply semi-supervised learning techniques, such as transfer learning or meta-learning. In this work we tackle Named Entity Recognition (NER) task using Prototypical Network - a metric learning technique. It learns intermediate representations of words which cluster well into named entity classes. This property of the model allows classifying words with extremely limited number of training examples, and can potentially be used as a zero-shot learning method. By coupling this technique with transfer learning we achieve well-performing classifiers trained on only 20 instances of a target class.

In proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

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Cited by in corpus (24)

• Named Entity Recognition and Classification on Historical Documents: A Survey
• Few-Shot Named Entity Recognition: A Comprehensive Study
• Controllable Semantic Parsing via Retrieval Augmentation
• Example-Based Named Entity Recognition
• Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study
• From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts
• Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning
• Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding
• Sensitive Data Detection with High-Throughput Neural Network Models for Financial Institutions
• ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER
• MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging
• Few-NERD: A Few-Shot Named Entity Recognition Dataset
• Low-Resource Name Tagging Learned with Weakly Labeled Data
• Modeling Token-level Uncertainty to Learn Unknown Concepts in SLU via Calibrated Dirichlet Prior RNN
• Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction
• What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data
• Augmented Natural Language for Generative Sequence Labeling
• Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF
• Linguistically-Enriched and Context-Aware Zero-shot Slot Filling
• Few-shot Intent Classification and Slot Filling with Retrieved Examples
• Toward Open-domain Slot Filling via Self-supervised Co-training
• Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language Models
• Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition
• Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework