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| license: mit |
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| # HuffPost Relation-Enriched Dataset for Machine Learning |
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| ## Description |
| This dataset is a semantically enriched version of the original HuffPost News Category dataset. It has been specifically re-engineered **Machine-Learning** tasks. |
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| Unlike the original dataset, this version incorporates explicit semantic relationships and concept embeddings to help models generalize better with very few examples. |
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| ## Key Enhancements |
| * **Relation-Centric Formatting**: Each entry is structured to support the `[ENTITY] [IS_A] [CATEGORY]` relationship. |
| * **Semantic Expansion**: Includes enriched context derived from semantic analysis (WordNet/Lesk integration) to provide better conceptual grounding. |
| * **Concept Vector Alignment**: Prepared for use with hybrid embedding architectures (e.g., combining MiniLM/BERT with specialized concept vectors). |
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| ## Intended Use |
| This dataset is designed for researchers working on: |
| * **Deep Learning Algorithms**: Specifically for fast adaptation in NLP. |
| * **Fusion Models**: Testing how dynamic fusion models balance text vs. conceptual information. |
| * **Relation Extraction**: Benchmarking models on news-domain hierarchical relationships. |
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| ## Technical Justification for GPU Grant |
| The enrichment process and the subsequent training on this dataset using **ML** involve complex gradient tracking and high-dimensional fusion (Dynamic Gated Fusion). This requires significant VRAM to handle the inner-loop adaptation across multiple tasks. |
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| ## Sources |
| * **Original Dataset**: [rmisra/news-category-dataset](https://huggingface.co/datasets/rmisra/news-category-dataset) |
| * **Modifications**: Semantic enrichment and relation-centric pooling by [Ton-Pseudo] |
