Whom to Learn From? Graph- vs. Text-based Word Embeddings

Małgorzata Salawa, António Branco, Ruben Branco, João António Rodrigues, Chakaveh Saedi


Abstract
Vectorial representations of meaning can be supported by empirical data from diverse sources and obtained with diverse embedding approaches. This paper aims at screening this experimental space and reports on an assessment of word embeddings supported (i) by data in raw texts vs. in lexical graphs, (ii) by lexical information encoded in association- vs. inference-based graphs, and obtained (iii) by edge reconstruction- vs. matrix factorisation vs. random walk-based graph embedding methods. The results observed with these experiments indicate that the best solutions with graph-based word embeddings are very competitive, consistently outperforming mainstream text-based ones.
Anthology ID:
R19-1120
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1041–1051
Language:
URL:
https://www.aclweb.org/anthology/R19-1120
DOI:
10.26615/978-954-452-056-4_120
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PDF:
http://aclanthology.lst.uni-saarland.de/R19-1120.pdf