Hierarchical Learning Applied to Word Sense Disambiguation
6 Pages Posted: 28 Apr 2017
Date Written: October 10, 2016
Abstract
This paper introduces a form of Hierarchical Learning that permits highly relevant association rules to be extracted from data items ambiguously related to a hierarchy. Addressing the problem of word sense disambiguation in natural language processing, this paper shows how references between words and hypernymy hierarchies may be used to generate highly relevant general rules representing valid associations that can thereafter be used to disambiguate unseen text.
Keywords: Natural Language Processing; WordNet; Hierarchical Learning; Worde-Sense Disambiguation; Unsupervised Learning
JEL Classification: C63,C65
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