A fast and lightweight system for multilingual dependency parsing

Tao Ji; Yuanbin Wu; Man Lan

doi:10.18653/v1/k17-3025

A fast and lightweight system for multilingual dependency parsing

Tao Ji
, Yuanbin Wu
, Man Lan

计算机科学与技术学院

East China Normal University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

Following Kiperwasser and Goldberg (2016), we present a multilingual dependency parser with a bidirectional-LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier. We trained our transition-based projective parser in UD version 2.0 datasets without any additional data. The parser is fast, lightweight and effective on big treebanks. In the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, the official results show that the macro-averaged LAS F1 score of our system Mengest is 61.33%.

源语言	英语
主期刊名	CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task
主期刊副标题	Multilingual Parsing from Raw Text to Universal Dependencies
出版商	Association for Computational Linguistics (ACL)
页	237-242
页数	6
ISBN（电子版）	9781945626708
DOI	https://doi.org/10.18653/v1/k17-3025
出版状态	已出版 - 2017
活动	2017 SIGNLL Conference on Computational Natural Language Learning- CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, CoNLL 2017 - Vancouver, 加拿大期限: 3 8月 2017 → 4 8月 2017

出版系列

姓名	CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

会议

会议	2017 SIGNLL Conference on Computational Natural Language Learning- CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, CoNLL 2017
国家/地区	加拿大
市	Vancouver
时期	3/08/17 → 4/08/17

访问文件

10.18653/v1/k17-3025

其它文件与链接

链接到 Scopus 的出版物

引用此

Ji, T., Wu, Y., & Lan, M. (2017). A fast and lightweight system for multilingual dependency parsing. 在 CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (页码 237-242). (CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/k17-3025

Ji, Tao ; Wu, Yuanbin ; Lan, Man. / A fast and lightweight system for multilingual dependency parsing. CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Association for Computational Linguistics (ACL), 2017. 页码 237-242 (CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies).

@inproceedings{124bbdcc60ba4cf9bc6251e19ec1c7ef,

title = "A fast and lightweight system for multilingual dependency parsing",

abstract = "Following Kiperwasser and Goldberg (2016), we present a multilingual dependency parser with a bidirectional-LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier. We trained our transition-based projective parser in UD version 2.0 datasets without any additional data. The parser is fast, lightweight and effective on big treebanks. In the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, the official results show that the macro-averaged LAS F1 score of our system Mengest is 61.33\%.",

author = "Tao Ji and Yuanbin Wu and Man Lan",

note = "Publisher Copyright: {\textcopyright} 2017 Association for Computational Linguistics.; 2017 SIGNLL Conference on Computational Natural Language Learning- CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, CoNLL 2017 ; Conference date: 03-08-2017 Through 04-08-2017",

year = "2017",

doi = "10.18653/v1/k17-3025",

language = "英语",

series = "CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",

publisher = "Association for Computational Linguistics (ACL)",

pages = "237--242",

booktitle = "CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task",

address = "澳大利亚",

}

Ji, T, Wu, Y & Lan, M 2017, A fast and lightweight system for multilingual dependency parsing. 在 CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, Association for Computational Linguistics (ACL), 页码 237-242, 2017 SIGNLL Conference on Computational Natural Language Learning- CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, CoNLL 2017, Vancouver, 加拿大, 3/08/17. https://doi.org/10.18653/v1/k17-3025

A fast and lightweight system for multilingual dependency parsing. / Ji, Tao; Wu, Yuanbin; Lan, Man.
CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Association for Computational Linguistics (ACL), 2017. 页码 237-242 (CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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AU - Ji, Tao

AU - Wu, Yuanbin

AU - Lan, Man

PY - 2017

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N2 - Following Kiperwasser and Goldberg (2016), we present a multilingual dependency parser with a bidirectional-LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier. We trained our transition-based projective parser in UD version 2.0 datasets without any additional data. The parser is fast, lightweight and effective on big treebanks. In the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, the official results show that the macro-averaged LAS F1 score of our system Mengest is 61.33%.

AB - Following Kiperwasser and Goldberg (2016), we present a multilingual dependency parser with a bidirectional-LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier. We trained our transition-based projective parser in UD version 2.0 datasets without any additional data. The parser is fast, lightweight and effective on big treebanks. In the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, the official results show that the macro-averaged LAS F1 score of our system Mengest is 61.33%.

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DO - 10.18653/v1/k17-3025

M3 - 会议稿件

AN - SCOPUS:85073109554

T3 - CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

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PB - Association for Computational Linguistics (ACL)

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Ji T, Wu Y, Lan M. A fast and lightweight system for multilingual dependency parsing. 在 CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Association for Computational Linguistics (ACL). 2017. 页码 237-242. (CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies). doi: 10.18653/v1/k17-3025

A fast and lightweight system for multilingual dependency parsing

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