https://github.com/luka-group/ddr
공식 코드Dense Retrieval as Indirect Supervision for Large-space Decision Making
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Dense Retrieval as Indirect Supervision for Large-space Decision Making
Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference
Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning
Decomposed Meta-Learning for Few-Shot Named Entity Recognition
EntEval: A Holistic Evaluation Benchmark for Entity Representations
$μ\text{KG}$: A Library for Multi-source Knowledge Graph Embeddings and Applications
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification
MTab: Matching Tabular Data to Knowledge Graph using Probability Models
MTab: Matching Tabular Data to Knowledge Graph using Probability Models
The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Representation Learning of Entities and Documents from Knowledge Base Descriptions
Learning with Different Amounts of Annotation: From Zero to Many Labels
Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generation
ERNIE: Enhanced Language Representation with Informative Entities
PromptNER: Prompt Locating and Typing for Named Entity Recognition
Representation Learning of Entities and Documents from Knowledge Base Descriptions
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing
Learning to Denoise Distantly-Labeled Data for Entity Typing
Fine-Grained Entity Typing for Domain Independent Entity Linking
Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity Typing
Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.