WordNet Embeddings

Chakaveh Saedi, António Branco, João António Rodrigues, João Silva


Abstract
Semantic networks and semantic spaces have been two prominent approaches to represent lexical semantics. While a unified account of the lexical meaning relies on one being able to convert between these representations, in both directions, the conversion direction from semantic networks into semantic spaces started to attract more attention recently. In this paper we present a methodology for this conversion and assess it with a case study. When it is applied over WordNet, the performance of the resulting embeddings in a mainstream semantic similarity task is very good, substantially superior to the performance of word embeddings based on very large collections of texts like word2vec.
Anthology ID:
W18-3016
Volume:
Proceedings of The Third Workshop on Representation Learning for NLP
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | RepL4NLP | WS
SIG:
SIGREP
Publisher:
Association for Computational Linguistics
Note:
Pages:
122–131
Language:
URL:
https://www.aclweb.org/anthology/W18-3016
DOI:
10.18653/v1/W18-3016
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-3016.pdf