@inproceedings{yin-etal-2017-chinese,
title = "{C}hinese Zero Pronoun Resolution with Deep Memory Network",
author = "Yin, Qingyu and
Zhang, Yu and
Zhang, Weinan and
Liu, Ting",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1135",
doi = "10.18653/v1/D17-1135",
pages = "1309--1318",
abstract = "Existing approaches for Chinese zero pronoun resolution typically utilize only syntactical and lexical features while ignoring semantic information. The fundamental reason is that zero pronouns have no descriptive information, which brings difficulty in explicitly capturing their semantic similarities with antecedents. Meanwhile, representing zero pronouns is challenging since they are merely gaps that convey no actual content. In this paper, we address this issue by building a deep memory network that is capable of encoding zero pronouns into vector representations with information obtained from their contexts and potential antecedents. Consequently, our resolver takes advantage of semantic information by using these continuous distributed representations. Experiments on the OntoNotes 5.0 dataset show that the proposed memory network could substantially outperform the state-of-the-art systems in various experimental settings.",
}
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<abstract>Existing approaches for Chinese zero pronoun resolution typically utilize only syntactical and lexical features while ignoring semantic information. The fundamental reason is that zero pronouns have no descriptive information, which brings difficulty in explicitly capturing their semantic similarities with antecedents. Meanwhile, representing zero pronouns is challenging since they are merely gaps that convey no actual content. In this paper, we address this issue by building a deep memory network that is capable of encoding zero pronouns into vector representations with information obtained from their contexts and potential antecedents. Consequently, our resolver takes advantage of semantic information by using these continuous distributed representations. Experiments on the OntoNotes 5.0 dataset show that the proposed memory network could substantially outperform the state-of-the-art systems in various experimental settings.</abstract>
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%0 Conference Proceedings
%T Chinese Zero Pronoun Resolution with Deep Memory Network
%A Yin, Qingyu
%A Zhang, Yu
%A Zhang, Weinan
%A Liu, Ting
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F yin-etal-2017-chinese
%X Existing approaches for Chinese zero pronoun resolution typically utilize only syntactical and lexical features while ignoring semantic information. The fundamental reason is that zero pronouns have no descriptive information, which brings difficulty in explicitly capturing their semantic similarities with antecedents. Meanwhile, representing zero pronouns is challenging since they are merely gaps that convey no actual content. In this paper, we address this issue by building a deep memory network that is capable of encoding zero pronouns into vector representations with information obtained from their contexts and potential antecedents. Consequently, our resolver takes advantage of semantic information by using these continuous distributed representations. Experiments on the OntoNotes 5.0 dataset show that the proposed memory network could substantially outperform the state-of-the-art systems in various experimental settings.
%R 10.18653/v1/D17-1135
%U https://aclanthology.org/D17-1135
%U https://doi.org/10.18653/v1/D17-1135
%P 1309-1318
Markdown (Informal)
[Chinese Zero Pronoun Resolution with Deep Memory Network](https://aclanthology.org/D17-1135) (Yin et al., EMNLP 2017)
ACL
- Qingyu Yin, Yu Zhang, Weinan Zhang, and Ting Liu. 2017. Chinese Zero Pronoun Resolution with Deep Memory Network. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1309–1318, Copenhagen, Denmark. Association for Computational Linguistics.