%0 Conference Proceedings
%T Whom to Learn From? Graph- vs. Text-based Word Embeddings
%A Salawa, Małgorzata
%A Branco, António
%A Branco, Ruben
%A António Rodrigues, João
%A Saedi, Chakaveh
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F salawa-etal-2019-learn
%X 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.
%R 10.26615/978-954-452-056-4_120
%U https://aclanthology.org/R19-1120
%U https://doi.org/10.26615/978-954-452-056-4_120
%P 1041-1051