@inproceedings{king-etal-2018-unbnlp,
title = "{UNBNLP} at {S}em{E}val-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes",
author = "King, Milton and
Hakimi Parizi, Ali and
Cook, Paul",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
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/S18-1168",
doi = "10.18653/v1/S18-1168",
pages = "1013--1016",
abstract = "In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model.",
}
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<abstract>In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model.</abstract>
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%0 Conference Proceedings
%T UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes
%A King, Milton
%A Hakimi Parizi, Ali
%A Cook, Paul
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F king-etal-2018-unbnlp
%X In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model.
%R 10.18653/v1/S18-1168
%U https://aclanthology.org/S18-1168
%U https://doi.org/10.18653/v1/S18-1168
%P 1013-1016
Markdown (Informal)
[UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes](https://aclanthology.org/S18-1168) (King et al., SemEval 2018)
ACL