@inproceedings{qiu-etal-2018-nlp,
title = "{NLP}{\_}{HZ} at {S}em{E}val-2018 Task 9: a Nearest Neighbor Approach",
author = "Qiu, Wei and
Chen, Mosha and
Li, Linlin and
Si, Luo",
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/S18-1148",
doi = "10.18653/v1/S18-1148",
pages = "909--913",
abstract = "Hypernym discovery aims to discover the hypernym word sets given a hyponym word and proper corpus. This paper proposes a simple but effective method for the discovery of hypernym sets based on word embedding, which can be used to measure the contextual similarities between words. Given a test hyponym word, we get its hypernym lists by computing the similarities between the hyponym word and words in the training data, and fill the test word{'}s hypernym lists with the hypernym list in the training set of the nearest similarity distance to the test word. In SemEval 2018 task9, our results, achieve 1st on Spanish, 2nd on Italian, 6th on English in the metric of MAP.",
}
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%0 Conference Proceedings
%T NLP_HZ at SemEval-2018 Task 9: a Nearest Neighbor Approach
%A Qiu, Wei
%A Chen, Mosha
%A Li, Linlin
%A Si, Luo
%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 qiu-etal-2018-nlp
%X Hypernym discovery aims to discover the hypernym word sets given a hyponym word and proper corpus. This paper proposes a simple but effective method for the discovery of hypernym sets based on word embedding, which can be used to measure the contextual similarities between words. Given a test hyponym word, we get its hypernym lists by computing the similarities between the hyponym word and words in the training data, and fill the test word’s hypernym lists with the hypernym list in the training set of the nearest similarity distance to the test word. In SemEval 2018 task9, our results, achieve 1st on Spanish, 2nd on Italian, 6th on English in the metric of MAP.
%R 10.18653/v1/S18-1148
%U https://aclanthology.org/S18-1148
%U https://doi.org/10.18653/v1/S18-1148
%P 909-913
Markdown (Informal)
[NLP_HZ at SemEval-2018 Task 9: a Nearest Neighbor Approach](https://aclanthology.org/S18-1148) (Qiu et al., SemEval 2018)
ACL