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.
|Number of pages||6|
|Journal||IJCAI International Joint Conference on Artificial Intelligence|
|Publication status||Published - 2003|