https://github.com/malteos/pytorch-bert-document-classification
공식 코드Enriching BERT with Knowledge Graph Embeddings for Document Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Enriching BERT with Knowledge Graph Embeddings for Document Classification
Knowledge-augmented Pre-trained Language Models for Biomedical Relation Extraction
DARWIN Series: Domain Specific Large Language Models for Natural Science
DARWIN Series: Domain Specific Large Language Models for Natural Science
A Critical Assessment of State-of-the-Art in Entity Alignment
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
On-Demand and Lightweight Knowledge Graph Generation -- a Demonstration with DBpedia
Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs
CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era
Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding
Knowledge Graph Embedding for Link Prediction: A Comparative Analysis
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
Fusing Context Into Knowledge Graph for Commonsense Question Answering
Context-aware knowledge graph framework for traffic speed forecasting using graph neural network
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs
WordCraft: An Environment for Benchmarking Commonsense Agents
Rethinking Graph Convolutional Networks in Knowledge Graph Completion
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs
When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context
Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation
Relational Graph Convolutional Networks Do Not Learn Sound Rules
Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.