@inproceedings{wang-etal-2017-transductive,
title = "Transductive Non-linear Learning for {C}hinese Hypernym Prediction",
author = "Wang, Chengyu and
Yan, Junchi and
Zhou, Aoying and
He, Xiaofeng",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1128",
doi = "10.18653/v1/P17-1128",
pages = "1394--1404",
abstract = "Finding the correct hypernyms for entities is essential for taxonomy learning, fine-grained entity categorization, query understanding, etc. Due to the flexibility of the Chinese language, it is challenging to identify hypernyms in Chinese accurately. Rather than extracting hypernyms from texts, in this paper, we present a transductive learning approach to establish mappings from entities to hypernyms in the embedding space directly. It combines linear and non-linear embedding projection models, with the capacity of encoding arbitrary language-specific rules. Experiments on real-world datasets illustrate that our approach outperforms previous methods for Chinese hypernym prediction.",
}
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<abstract>Finding the correct hypernyms for entities is essential for taxonomy learning, fine-grained entity categorization, query understanding, etc. Due to the flexibility of the Chinese language, it is challenging to identify hypernyms in Chinese accurately. Rather than extracting hypernyms from texts, in this paper, we present a transductive learning approach to establish mappings from entities to hypernyms in the embedding space directly. It combines linear and non-linear embedding projection models, with the capacity of encoding arbitrary language-specific rules. Experiments on real-world datasets illustrate that our approach outperforms previous methods for Chinese hypernym prediction.</abstract>
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%0 Conference Proceedings
%T Transductive Non-linear Learning for Chinese Hypernym Prediction
%A Wang, Chengyu
%A Yan, Junchi
%A Zhou, Aoying
%A He, Xiaofeng
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F wang-etal-2017-transductive
%X Finding the correct hypernyms for entities is essential for taxonomy learning, fine-grained entity categorization, query understanding, etc. Due to the flexibility of the Chinese language, it is challenging to identify hypernyms in Chinese accurately. Rather than extracting hypernyms from texts, in this paper, we present a transductive learning approach to establish mappings from entities to hypernyms in the embedding space directly. It combines linear and non-linear embedding projection models, with the capacity of encoding arbitrary language-specific rules. Experiments on real-world datasets illustrate that our approach outperforms previous methods for Chinese hypernym prediction.
%R 10.18653/v1/P17-1128
%U https://aclanthology.org/P17-1128
%U https://doi.org/10.18653/v1/P17-1128
%P 1394-1404
Markdown (Informal)
[Transductive Non-linear Learning for Chinese Hypernym Prediction](https://aclanthology.org/P17-1128) (Wang et al., ACL 2017)
ACL
- Chengyu Wang, Junchi Yan, Aoying Zhou, and Xiaofeng He. 2017. Transductive Non-linear Learning for Chinese Hypernym Prediction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1394–1404, Vancouver, Canada. Association for Computational Linguistics.