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Taxonomy Learning
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Taxonomy learning is the task of hierarchically classifying concepts in an automatic manner from text corpora. The process of building taxonomies is usually divided into two main steps: (1) extracting hypernyms for concepts, which may constitute a field of research in itself (see Hypernym Discovery below) and (2) refining the structure into a taxonomy.
Description from NLP Progress
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Rank, Chunk and Expand: Lineage-Oriented Reasoning for Taxonomy Expansion
OntoTune: Ontology-Driven Self-training for Aligning Large Language Models
QuanTaxo: A Quantum Approach to Self-Supervised Taxonomy Expansion
CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts
Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysis
TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks
FLAME: Self-Supervised Low-Resource Taxonomy Expansion using Large Language Models
A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion
Probing Pretrained Language Models with Hierarchy Properties
Towards Visual Taxonomy Expansion
Taxonomy Expansion for Named Entity Recognition
GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation
DNG: Taxonomy Expansion by Exploring the Intrinsic Directed Structure on Non-gaussian Space
Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
Modelling Commonsense Properties using Pre-Trained Bi-Encoders
Flexible Job Classification with Zero-Shot Learning
HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings
Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision
HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning
Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations
Document Structure aware Relational Graph Convolutional Networks for Ontology Population
Who Should Go First? A Self-Supervised Concept Sorting Model for Improving Taxonomy Expansion
Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion
Taxonomy Completion via Triplet Matching Network
STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
Hyperbolic Manifold Regression
TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings
A Consolidated System for Robust Multi-Document Entity Risk Extraction and Taxonomy Augmentation
Document Structure Measure for Hypernym discovery
SJTU-NLP at SemEval-2018 Task 9: Neural Hypernym Discovery with Term Embeddings
UMDuluth-CS8761 at SemEval-2018 Task 9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection
Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection
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