Document Structure Measure for Hypernym discovery
This addresses hypernym discovery for natural language processing, but appears incremental as it builds on existing context and relatedness methods.
The paper tackles hypernym discovery by introducing a Document Structure measure based on hierarchical document position and definition text presence, achieving differentiation of hypernyms from other semantic relationships.
Hypernym discovery is the problem of finding terms that have is-a relationship with a given term. We introduce a new context type, and a relatedness measure to differentiate hypernyms from other types of semantic relationships. Our Document Structure measure is based on hierarchical position of terms in a document, and their presence or otherwise in definition text. This measure quantifies the document structure using multiple attributes, and classes of weighted distance functions.