https://github.com/yyaghoobzadeh/MVET
공식 코드Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
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.
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
ERNIE: Enhanced Language Representation with Informative Entities
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.