Cortex uses an Ontological Corpus Graph to structure web-scale corpora, creating a refined 24.14B-token corpus and a new benchmark validated on eight LLMs.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , pages =
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A term-centric framework uses automatic term extraction to align heterogeneous document collections into a shared space and builds hierarchies by combining domain priors with clustering, outperforming document-level baselines on a 1M+ document English-German benchmark.
Filtering job posting data before LLM-assisted clustering and hierarchical labeling yields taxonomies with better AI skill coverage than unfiltered approaches.
The WaveletInception-BiGRU network uses learnable wavelet packet transforms, 1D Inception-ResNet modules, and BiGRU layers to generate high-resolution, spatially mapped health profiles from variable-speed vibration data, outperforming prior methods on track stiffness and transition zone tasks.
citing papers explorer
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CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph
Cortex uses an Ontological Corpus Graph to structure web-scale corpora, creating a refined 24.14B-token corpus and a new benchmark validated on eight LLMs.
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Term-Centric Hierarchy Induction from Heterogeneous Corpora
A term-centric framework uses automatic term extraction to align heterogeneous document collections into a shared space and builds hierarchies by combining domain priors with clustering, outperforming document-level baselines on a 1M+ document English-German benchmark.
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Building a Custom Taxonomy of AI Skills and Tasks from the Ground Up with Job Postings
Filtering job posting data before LLM-assisted clustering and hierarchical labeling yields taxonomies with better AI skill coverage than unfiltered approaches.
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WaveletInception Networks for on-board Vibration-Based Infrastructure Health Monitoring
The WaveletInception-BiGRU network uses learnable wavelet packet transforms, 1D Inception-ResNet modules, and BiGRU layers to generate high-resolution, spatially mapped health profiles from variable-speed vibration data, outperforming prior methods on track stiffness and transition zone tasks.