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.
Proceedings of the ACM Web Conference 2022 , pages =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
GS-Quant generates coarse-to-fine discrete codes for KG entities via semantic hierarchy injection and causal sequence reconstruction, enabling LLMs to perform knowledge graph completion by treating the codes as vocabulary tokens.
citing papers explorer
-
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.
-
GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion
GS-Quant generates coarse-to-fine discrete codes for KG entities via semantic hierarchy injection and causal sequence reconstruction, enabling LLMs to perform knowledge graph completion by treating the codes as vocabulary tokens.