Probabilistic graph-based dependency parsing with convolutional neural network

Zhisong Zhang; Hai Zhao; Lianhui Qin

doi:10.18653/v1/p16-1131

Probabilistic graph-based dependency parsing with convolutional neural network

Zhisong Zhang, Hai Zhao, Lianhui Qin

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

56 Citations (Scopus)

2 Downloads (CityUHK Scholars)

Abstract

This paper presents neural probabilistic parsing models which explore up to thirdorder graph-based parsing with maximum likelihood training criteria. Two neural network extensions are exploited for performance improvement. Firstly, a convolutional layer that absorbs the influences of all words in a sentence is used so that sentence-level information can be effectively captured. Secondly, a linear layer is added to integrate different order neural models and trained with perceptron method. The proposed parsers are evaluated on English and Chinese Penn Treebanks and obtain competitive accuracies.

Original language	English
Title of host publication	54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers
Publisher	ACL Anthology
Pages	1382-1392
Volume	3
ISBN (Print)	9781510827585
DOIs	https://doi.org/10.18653/v1/p16-1131
Publication status	Published - 2016
Externally published	Yes
Event	54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Berlin, Germany Duration: 7 Aug 2016 → 12 Aug 2016

Publication series

Name	54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers
Volume	3

Conference

Conference	54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
Place	Germany
City	Berlin
Period	7/08/16 → 12/08/16

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to <a href="mailto:[email protected]">[email protected]</a>.

Funding

This paper was partially supported by Cai Yuanpei Program (CSC No. 201304490199 and No. 201304490171), National Natural Science Foundation of China (No. 61170114 and No. 61272248), National Basic Research Program of China (No. 2013CB329401), Major Basic Research Program of Shanghai Science and Technology Committee (No. 15JC1400103), Art and Science Interdisciplinary Funds of Shanghai Jiao Tong University (No. 14JCRZ04), and Key Project of National Society Science Foundation of China (No. 15-ZDA041).

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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Zhang, Z., Zhao, H., & Qin, L. (2016). Probabilistic graph-based dependency parsing with convolutional neural network. In 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers (Vol. 3, pp. 1382-1392). (54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers; Vol. 3). ACL Anthology. https://doi.org/10.18653/v1/p16-1131

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Zhang, Z, Zhao, H & Qin, L 2016, Probabilistic graph-based dependency parsing with convolutional neural network. in 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers. vol. 3, 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers, vol. 3, ACL Anthology, pp. 1382-1392, 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, Berlin, Germany, 7/08/16. https://doi.org/10.18653/v1/p16-1131

Probabilistic graph-based dependency parsing with convolutional neural network. / Zhang, Zhisong; Zhao, Hai; Qin, Lianhui.
54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers. Vol. 3 ACL Anthology, 2016. p. 1382-1392 (54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers; Vol. 3).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AB - This paper presents neural probabilistic parsing models which explore up to thirdorder graph-based parsing with maximum likelihood training criteria. Two neural network extensions are exploited for performance improvement. Firstly, a convolutional layer that absorbs the influences of all words in a sentence is used so that sentence-level information can be effectively captured. Secondly, a linear layer is added to integrate different order neural models and trained with perceptron method. The proposed parsers are evaluated on English and Chinese Penn Treebanks and obtain competitive accuracies.

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Probabilistic graph-based dependency parsing with convolutional neural network

Abstract

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