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.
Large-scale learning of word relatedness with constraints
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Supervised classifiers (SVM, k-NN, deep learning) trained on 5,574 annotated Java/.NET API documents achieve up to 87% AUPRC for single knowledge types and 79% MacroAUC for multi-label classification, with partial generalization to Python documentation.
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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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On Using Machine Learning to Identify Knowledge in API Reference Documentation
Supervised classifiers (SVM, k-NN, deep learning) trained on 5,574 annotated Java/.NET API documents achieve up to 87% AUPRC for single knowledge types and 79% MacroAUC for multi-label classification, with partial generalization to Python documentation.