Relational Word Embeddings

Jose Camacho-Collados, Luis Espinosa Anke, Steven Schockaert


Abstract
While word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited. In previous work, this limitation has been addressed by incorporating relational knowledge from external knowledge bases when learning the word embedding. Such strategies may not be optimal, however, as they are limited by the coverage of available resources and conflate similarity with other forms of relatedness. As an alternative, in this paper we propose to encode relational knowledge in a separate word embedding, which is aimed to be complementary to a given standard word embedding. This relational word embedding is still learned from co-occurrence statistics, and can thus be used even when no external knowledge base is available. Our analysis shows that relational word vectors do indeed capture information that is complementary to what is encoded in standard word embeddings.
Anthology ID:
P19-1318
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3286–3296
Language:
URL:
https://aclanthology.org/P19-1318
DOI:
10.18653/v1/P19-1318
Bibkey:
Cite (ACL):
Jose Camacho-Collados, Luis Espinosa Anke, and Steven Schockaert. 2019. Relational Word Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3286–3296, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Relational Word Embeddings (Camacho-Collados et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1318.pdf
Code
 pedrada88/rwe
Data
HyperLex