@inproceedings{gupta-etal-2017-distributed,
title = "Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible",
author = "Gupta, Abhijeet and
Boleda, Gemma and
Pad\'o, Sebastian",
editor = "Ide, Nancy and
Herbelot, Aur\'elie and
M\`arquez, Llu\'\i s",
booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-1012/",
doi = "10.18653/v1/S17-1012",
pages = "104--109",
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."
}
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%0 Conference Proceedings
%T Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible
%A Gupta, Abhijeet
%A Boleda, Gemma
%A Padó, Sebastian
%Y Ide, Nancy
%Y Herbelot, Aurélie
%Y Màrquez, Lluí s.
%S Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F gupta-etal-2017-distributed
%X 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.
%R 10.18653/v1/S17-1012
%U https://aclanthology.org/S17-1012/
%U https://doi.org/10.18653/v1/S17-1012
%P 104-109
Markdown (Informal)
[Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible](https://aclanthology.org/S17-1012/) (Gupta et al., *SEM 2017)
ACL