Hierarchical semantic classification

Word sense disambiguation with world knowledge

Massimiliano Ciaramita, Thomas Hofmann, Mark Johnson

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

We present a learning architecture for lexical semantic classification problems that supplements task-specific training data with background data encoding general "world knowledge". The model compiles knowledge contained in a dictionary-ontology into additional training data, and integrates task-specific and background data through a novel hierarchical learning architecture. Experiments on a word sense disambiguation task provide empirical evidence that this "hierarchical classifier" outperforms a state-of-the-art standard "flat" one.

Original languageEnglish
Pages (from-to)817-822
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
Publication statusPublished - 2003
Externally publishedYes

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