Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible

Abhijeet Gupta, Gemma Boleda, Sebastian Padó


Abstract
Word embeddings are supposed to provide easy access to semantic relations such as “male of” (man–woman). While this claim has been investigated for concepts, little is known about the distributional behavior of relations of (Named) Entities. We describe two word embedding-based models that predict values for relational attributes of entities, and analyse them. The task is challenging, with major performance differences between relations. Contrary to many NLP tasks, high difficulty for a relation does not result from low frequency, but from (a) one-to-many mappings; and (b) lack of context patterns expressing the relation that are easy to pick up by word embeddings.
Anthology ID:
S17-1012
Volume:
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
104–109
Language:
URL:
https://www.aclweb.org/anthology/S17-1012
DOI:
10.18653/v1/S17-1012
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-1012.pdf