Entity Typing
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Entity Typing is an important task in text analysis. Assigning types (e.g., person, location, organization) to mentions of entities in documents enables effective structured analysis of unstructured text corpora. The extracted type information can be used in a wide range of ways (e.g., serving as primitives for information extraction and knowledge base (KB) completion, and assisting question answering). Traditional Entity Typing systems focus on a small set of coarse types (typically fewer than 10). Recent studies work on a much larger set of fine-grained types which form a tree-structured hierarchy (e.g., actor as a subtype of artist, and artist is a subtype of person).
Source: Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
Image Credit: Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Refining Wikidata Taxonomy using Large Language Models
GeoReasoner: Reasoning On Geospatially Grounded Context For Natural Language Understanding
Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian
COTET: Cross-view Optimal Transport for Knowledge Graph Entity Typing
REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing
Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification
Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains
ConcEPT: Concept-Enhanced Pre-Training for Language Models
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification
From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained
Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing
Dense Retrieval as Indirect Supervision for Large-space Decision Making
GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning
What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies
Do Language Models Learn about Legal Entity Types during Pretraining?
Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs
Ontology Enrichment for Effective Fine-grained Entity Typing
SLHCat: Mapping Wikipedia Categories and Lists to DBpedia by Leveraging Semantic, Lexical, and Hierarchical Features
AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations
SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding
PromptNER: Prompt Locating and Typing for Named Entity Recognition
Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains
OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing
UNTER: A Unified Knowledge Interface for Enhancing Pre-trained Language Models
Dynamic Named Entity Recognition
Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing
Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field
Learning to Select from Multiple Options
CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification
Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity Typing
Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.