@inproceedings{chen-etal-2018-pre,
title = "Pre- and In-Parsing Models for Neural Empty Category Detection",
author = "Chen, Yufei and
Zhao, Yuanyuan and
Sun, Weiwei and
Wan, Xiaojun",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1250",
doi = "10.18653/v1/P18-1250",
pages = "2687--2696",
abstract = "Motivated by the positive impact of empty category on syntactic parsing, we study neural models for pre- and in-parsing detection of empty category, which has not previously been investigated. We find several non-obvious facts: (a) BiLSTM can capture non-local contextual information which is essential for detecting empty categories, (b) even with a BiLSTM, syntactic information is still able to enhance the detection, and (c) automatic detection of empty categories improves parsing quality for overt words. Our neural ECD models outperform the prior state-of-the-art by significant margins.",
}
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<abstract>Motivated by the positive impact of empty category on syntactic parsing, we study neural models for pre- and in-parsing detection of empty category, which has not previously been investigated. We find several non-obvious facts: (a) BiLSTM can capture non-local contextual information which is essential for detecting empty categories, (b) even with a BiLSTM, syntactic information is still able to enhance the detection, and (c) automatic detection of empty categories improves parsing quality for overt words. Our neural ECD models outperform the prior state-of-the-art by significant margins.</abstract>
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%0 Conference Proceedings
%T Pre- and In-Parsing Models for Neural Empty Category Detection
%A Chen, Yufei
%A Zhao, Yuanyuan
%A Sun, Weiwei
%A Wan, Xiaojun
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F chen-etal-2018-pre
%X Motivated by the positive impact of empty category on syntactic parsing, we study neural models for pre- and in-parsing detection of empty category, which has not previously been investigated. We find several non-obvious facts: (a) BiLSTM can capture non-local contextual information which is essential for detecting empty categories, (b) even with a BiLSTM, syntactic information is still able to enhance the detection, and (c) automatic detection of empty categories improves parsing quality for overt words. Our neural ECD models outperform the prior state-of-the-art by significant margins.
%R 10.18653/v1/P18-1250
%U https://aclanthology.org/P18-1250
%U https://doi.org/10.18653/v1/P18-1250
%P 2687-2696
Markdown (Informal)
[Pre- and In-Parsing Models for Neural Empty Category Detection](https://aclanthology.org/P18-1250) (Chen et al., ACL 2018)
ACL
- Yufei Chen, Yuanyuan Zhao, Weiwei Sun, and Xiaojun Wan. 2018. Pre- and In-Parsing Models for Neural Empty Category Detection. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2687–2696, Melbourne, Australia. Association for Computational Linguistics.