@inproceedings{gamallo-2018-citiusnlp,
title = "{C}itius{NLP} at {S}em{E}val-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes",
author = "Gamallo, Pablo",
booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1156",
doi = "10.18653/v1/S18-1156",
pages = "953--957",
abstract = "This article describes the unsupervised strategy submitted by the CitiusNLP team to the SemEval 2018 Task 10, a task which consists of predict whether a word is a discriminative attribute between two other words. Our strategy relies on the correspondence between discriminative attributes and relevant contexts of a word. More precisely, the method uses transparent distributional models to extract salient contexts of words which are identified as discriminative attributes. The system performance reaches about 70{\%} accuracy when it is applied on the development dataset, but its accuracy goes down (63{\%}) on the official test dataset.",
}
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<abstract>This article describes the unsupervised strategy submitted by the CitiusNLP team to the SemEval 2018 Task 10, a task which consists of predict whether a word is a discriminative attribute between two other words. Our strategy relies on the correspondence between discriminative attributes and relevant contexts of a word. More precisely, the method uses transparent distributional models to extract salient contexts of words which are identified as discriminative attributes. The system performance reaches about 70% accuracy when it is applied on the development dataset, but its accuracy goes down (63%) on the official test dataset.</abstract>
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%0 Conference Proceedings
%T CitiusNLP at SemEval-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes
%A Gamallo, Pablo
%S Proceedings of The 12th International Workshop on Semantic Evaluation
%D 2018
%8 jun
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F gamallo-2018-citiusnlp
%X This article describes the unsupervised strategy submitted by the CitiusNLP team to the SemEval 2018 Task 10, a task which consists of predict whether a word is a discriminative attribute between two other words. Our strategy relies on the correspondence between discriminative attributes and relevant contexts of a word. More precisely, the method uses transparent distributional models to extract salient contexts of words which are identified as discriminative attributes. The system performance reaches about 70% accuracy when it is applied on the development dataset, but its accuracy goes down (63%) on the official test dataset.
%R 10.18653/v1/S18-1156
%U https://aclanthology.org/S18-1156
%U https://doi.org/10.18653/v1/S18-1156
%P 953-957
Markdown (Informal)
[CitiusNLP at SemEval-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes](https://aclanthology.org/S18-1156) (Gamallo, SemEval 2018)
ACL