@inproceedings{glavas-vulic-2018-discriminating,
title = "Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model",
author = "Glava{\v{s}}, Goran and
Vuli{\'c}, Ivan",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2029",
doi = "10.18653/v1/N18-2029",
pages = "181--187",
abstract = "We present a simple and effective feed-forward neural architecture for discriminating between lexico-semantic relations (synonymy, antonymy, hypernymy, and meronymy). Our Specialization Tensor Model (STM) simultaneously produces multiple different specializations of input distributional word vectors, tailored for predicting lexico-semantic relations for word pairs. STM outperforms more complex state-of-the-art architectures on two benchmark datasets and exhibits stable performance across languages. We also show that, if coupled with a bilingual distributional space, the proposed model can transfer the prediction of lexico-semantic relations to a resource-lean target language without any training data.",
}
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%0 Conference Proceedings
%T Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model
%A Glavaš, Goran
%A Vulić, Ivan
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F glavas-vulic-2018-discriminating
%X We present a simple and effective feed-forward neural architecture for discriminating between lexico-semantic relations (synonymy, antonymy, hypernymy, and meronymy). Our Specialization Tensor Model (STM) simultaneously produces multiple different specializations of input distributional word vectors, tailored for predicting lexico-semantic relations for word pairs. STM outperforms more complex state-of-the-art architectures on two benchmark datasets and exhibits stable performance across languages. We also show that, if coupled with a bilingual distributional space, the proposed model can transfer the prediction of lexico-semantic relations to a resource-lean target language without any training data.
%R 10.18653/v1/N18-2029
%U https://aclanthology.org/N18-2029
%U https://doi.org/10.18653/v1/N18-2029
%P 181-187
Markdown (Informal)
[Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model](https://aclanthology.org/N18-2029) (Glavaš & Vulić, NAACL 2018)
ACL