ChainNet: Structured Metaphor and Metonymy in WordNet

TL;DR

ChainNet is a novel lexical resource that explicitly models the internal structure of word senses through metaphor and metonymy relations, enhancing the understanding of semantic derivations.

Contribution

It introduces ChainNet, the first dataset explicitly linking WordNet senses via metaphor and metonymy, revealing internal sense structures.

Findings

First dataset of grounded metaphor and metonymy in WordNet

Explicitly models sense derivations through metaphor and metonymy

Enhances semantic understanding of lexical resources

Abstract

The senses of a word exhibit rich internal structure. In a typical lexicon, this structure is overlooked: a word's senses are encoded as a list without inter-sense relations. We present ChainNet, a lexical resource which for the first time explicitly identifies these structures. ChainNet expresses how senses in the Open English Wordnet are derived from one another: every nominal sense of a word is either connected to another sense by metaphor or metonymy, or is disconnected in the case of homonymy. Because WordNet senses are linked to resources which capture information about their meaning, ChainNet represents the first dataset of grounded metaphor and metonymy.