Framework acquires descriptive text for entities via web and LLMs to train classifiers from names and labels alone, achieving 82.3% and 72.9% macro F1 on SIC code and healthcare taxonomy classification tasks.
InProceedings of the 6th International The Se- mantic Web and 2nd Asian Conference on Asian Semantic Web Conference, ISWC’07/ASWC’07, page 722–735, Berlin, Heidelberg
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks
Framework acquires descriptive text for entities via web and LLMs to train classifiers from names and labels alone, achieving 82.3% and 72.9% macro F1 on SIC code and healthcare taxonomy classification tasks.