@inproceedings{issa-alaa-aldine-etal-2018-expr,
title = "{EXPR} at {S}em{E}val-2018 Task 9: A Combined Approach for Hypernym Discovery",
author = "Issa Alaa Aldine, Ahmad and
Harzallah, Mounira and
Berio, Giuseppe and
B{\'e}chet, Nicolas and
Faour, Ahmad",
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/S18-1150",
doi = "10.18653/v1/S18-1150",
pages = "919--923",
abstract = "In this paper, we present our proposed system (EXPR) to participate in the hypernym discovery task of SemEval 2018. The task addresses the challenge of discovering hypernym relations from a text corpus. Our proposal is a combined approach of path-based technique and distributional technique. We use dependency parser on a corpus to extract candidate hypernyms and represent their dependency paths as a feature vector. The feature vector is concatenated with a feature vector obtained using Wikipedia pre-trained term embedding model. The concatenated feature vector fits a supervised machine learning method to learn a classifier model. This model is able to classify new candidate hypernyms as hypernym or not. Our system performs well to discover new hypernyms not defined in gold hypernyms.",
}
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<abstract>In this paper, we present our proposed system (EXPR) to participate in the hypernym discovery task of SemEval 2018. The task addresses the challenge of discovering hypernym relations from a text corpus. Our proposal is a combined approach of path-based technique and distributional technique. We use dependency parser on a corpus to extract candidate hypernyms and represent their dependency paths as a feature vector. The feature vector is concatenated with a feature vector obtained using Wikipedia pre-trained term embedding model. The concatenated feature vector fits a supervised machine learning method to learn a classifier model. This model is able to classify new candidate hypernyms as hypernym or not. Our system performs well to discover new hypernyms not defined in gold hypernyms.</abstract>
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%0 Conference Proceedings
%T EXPR at SemEval-2018 Task 9: A Combined Approach for Hypernym Discovery
%A Issa Alaa Aldine, Ahmad
%A Harzallah, Mounira
%A Berio, Giuseppe
%A Béchet, Nicolas
%A Faour, Ahmad
%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 issa-alaa-aldine-etal-2018-expr
%X In this paper, we present our proposed system (EXPR) to participate in the hypernym discovery task of SemEval 2018. The task addresses the challenge of discovering hypernym relations from a text corpus. Our proposal is a combined approach of path-based technique and distributional technique. We use dependency parser on a corpus to extract candidate hypernyms and represent their dependency paths as a feature vector. The feature vector is concatenated with a feature vector obtained using Wikipedia pre-trained term embedding model. The concatenated feature vector fits a supervised machine learning method to learn a classifier model. This model is able to classify new candidate hypernyms as hypernym or not. Our system performs well to discover new hypernyms not defined in gold hypernyms.
%R 10.18653/v1/S18-1150
%U https://aclanthology.org/S18-1150
%U https://doi.org/10.18653/v1/S18-1150
%P 919-923
Markdown (Informal)
[EXPR at SemEval-2018 Task 9: A Combined Approach for Hypernym Discovery](https://aclanthology.org/S18-1150) (Issa Alaa Aldine et al., SemEval 2018)
ACL