System for Medical Concept Extraction and Linking
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Updated
Aug 12, 2024 - Python
System for Medical Concept Extraction and Linking
CoCo-Ex extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph.
[ECCV 2024 Oral] ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extraction
Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
Flexible and powerful platform for biomedical information extraction from text
Code for the paper: Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery. ECCV 2024.
Explainability of Deep Learning Models
Tools for Formal Concept Analysis
MEME: Generating RNN Model Explanations via Model Extraction
CME: Concept-based Model Extraction
CoCo-Ex extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph.
Simple spaCy-based concept extraction API, involving a dictionary of relevant concepts.
Combining Energy-Based Modeling and RL to solve the challenging Abstract Reasoning Corpus[1] tasks.
create concept map from textbook data
Software created within Accumulate project (www.accumulate.be) at CLiPS, University of Antwerp
Retrospective Extraction of Visual and Logical Insights for Ontology-based interpretation of Neural Networks
Python code for construction and analysis of semantic networks from text.
REST-API for LearningMiner.
A toolkit to do concept expansion via search engine snippet
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