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Online large-margin training of dependency parsers

Authors:

Fernando PereiraAuthors Info & Claims

ACL '05: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics

Pages 91 - 98

https://doi.org/10.3115/1219840.1219852

Published: 25 June 2005 Publication History

Abstract

We present an effective training algorithm for linearly-scored dependency parsers that implements online large-margin multi-class training (Crammer and Singer, 2003; Crammer et al., 2003) on top of efficient parsing techniques for dependency trees (Eisner, 1996). The trained parsers achieve a competitive dependency accuracy for both English and Czech with no language specific enhancements.

References

[1]

D. M. Bikel. 2004. Intricacies of Collins parsing model. Computational Linguistics.

Digital Library

[2]

Y. Censor and S. A. Zenios. 1997. Parallel optimization: theory, algorithms, and applications. Oxford University Press.

Digital Library

[3]

E. Charniak. 2000. A maximum-entropy-inspired parser. In Proc. NAACL.

Digital Library

[4]

S. Clark and J. R. Curran. 2004. Parsing the WSJ using CCG and log-linear models. In Proc. ACL.

Digital Library

[5]

M. Collins and B. Roark. 2004. Incremental parsing with the perceptron algorithm. In Proc. ACL.

Digital Library

[6]

M. Collins, J. Hajič, L. Ramshaw, and C. Tillmann. 1999. A statistical parser for Czech. In Proc. ACL.

Digital Library

[7]

M. Collins. 1999. Head-Driven Statistical Models for Natural Language Parsing. Ph.D. thesis, University of Pennsylvania.

Digital Library

[8]

M. Collins. 2002. Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms. In Proc. EMNLP.

Digital Library

[9]

K. Crammer and Y. Singer. 2001. On the algorithmic implementation of multiclass kernel based vector machines. JMLR.

Digital Library

[10]

K. Crammer and Y. Singer. 2003. Ultraconservative on-line algorithms for multiclass problems. JMLR.

Digital Library

[11]

K. Crammer, O. Dekel, S. Shalev-Shwartz, and Y. Singer. 2003. Online passive aggressive algorithms. In Proc. NIPS.

[12]

A. Culotta and J. Sorensen. 2004. Dependency tree kernels for relation extraction. In Proc. ACL.

Digital Library

[13]

Y. Ding and M. Palmer. 2005. Machine translation using probabilistic synchronous dependency insertion grammars. In Proc. ACL.

Digital Library

[14]

J. Eisner and G. Satta. 1999. Efficient parsing for bilexical context-free grammars and head-automaton grammars. In Proc. ACL.

Digital Library

[15]

J. Eisner. 1996. Three new probabilistic models for dependency parsing: An exploration. In Proc. COLING.

Digital Library

[16]

J. Hajič. 1998. Building a syntactically annotated corpus: The Prague dependency treebank. Issues of Valency and Meaning.

[17]

L. Huang and D. Chiang. 2005. Better k-best parsing. Technical Report MS-CIS-05-08, University of Pennsylvania.

[18]

Richard Hudson. 1984. Word Grammar. Blackwell.

[19]

T. Joachims. 2002. Learning to Classify Text using Support Vector Machines. Kluwer.

Digital Library

[20]

J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. ICML.

Digital Library

[21]

M. Marcus, B. Santorini, and M. Marcinkiewicz. 1993. Building a large annotated corpus of english: the penn treebank. Computational Linguistics.

Digital Library

[22]

J. Nivre and M. Scholz. 2004. Deterministic dependency parsing of english text. In Proc. COLING.

Digital Library

[23]

A. Ratnaparkhi. 1996. A maximum entropy model for part-of-speech tagging. In Proc. EMNLP.

[24]

A. Ratnaparkhi. 1999. Learning to parse natural language with maximum entropy models. Machine Learning.

Digital Library

[25]

S. Riezler, T. King, R. Kaplan, R. Crouch, J. Maxwell, and M. Johnson. 2002. Parsing the Wall Street Journal using a lexical-functional grammar and discriminative estimation techniques. In Proc. ACL.

Digital Library

[26]

F. Sha and F. Pereira. 2003. Shallow parsing with conditional random fields. In Proc. HLT-NAACL.

Digital Library

[27]

Y. Shinyama, S. Sekine, K. Sudo, and R. Grishman. 2002. Automatic paraphrase acquisition from news articles. In Proc. HLT.

Digital Library

[28]

B. Taskar, C. Guestrin, and D. Koller. 2003. Max-margin Markov networks. In Proc. NIPS.

[29]

B. Taskar, D. Klein, M. Collins, D. Koller, and C. Manning. 2004. Max-margin parsing. In Proc. EMNLP.

[30]

H. Yamada and Y. Matsumoto. 2003. Statistical dependency analysis with support vector machines. In Proc. IWPT.

Cited By

Fernández-González DGómez-Rodríguez C(2023)Dependency parsing with bottom-up Hierarchical Pointer NetworksInformation Fusion10.1016/j.inffus.2022.10.02391:C(494-503)Online publication date: 1-Mar-2023
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Online large-margin training of dependency parsers

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cover image DL Hosted proceedings

ACL '05: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics

June 2005

657 pages

General Chair:
Kevin Knight
University of Southern California

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 25 June 2005

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ACL '05 Paper Acceptance Rate 77 of 423 submissions, 18%;

Overall Acceptance Rate 85 of 443 submissions, 19%

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Cited By

Fernández-González DGómez-Rodríguez C(2023)Dependency parsing with bottom-up Hierarchical Pointer NetworksInformation Fusion10.1016/j.inffus.2022.10.02391:C(494-503)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.inffus.2022.10.023
Taufiq UPulungan RSuyanto Y(2023)Named entity recognition and dependency parsing for better concept extraction in summary obfuscation detectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119579217:COnline publication date: 1-May-2023
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Wu LZhang M(2021)Deep Graph-Based Character-Level Chinese Dependency ParsingIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2021.306721229(1329-1339)Online publication date: 18-Mar-2021
https://dl.acm.org/doi/10.1109/TASLP.2021.3067212
Şahin GVania CKuznetsov IGurevych I(2020)LINSPECTORComputational Linguistics10.1162/coli_a_0037646:2(335-385)Online publication date: 1-Jun-2020
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Das ASarkar S(2020)A Survey of the Model Transfer Approaches to Cross-Lingual Dependency ParsingACM Transactions on Asian and Low-Resource Language Information Processing10.1145/338377219:5(1-60)Online publication date: 1-Jun-2020
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Bhattu SNunna SSomayajulu DPradhan B(2020)Improving Code-mixed POS Tagging Using Code-mixed EmbeddingsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/338096719:4(1-31)Online publication date: 29-Mar-2020
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Ponti EO’Horan HBerzak YVulić IReichart RPoibeau TShutova EKorhonen A(2019)Modeling Language Variation and UniversalsComputational Linguistics10.1162/coli_a_0035745:3(559-601)Online publication date: 1-Sep-2019
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Sun WChen YWan XLiu M(2019)Parsing chinese sentences with grammatical relationsComputational Linguistics10.1162/coli_a_0034345:1(95-136)Online publication date: 1-Mar-2019
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Jung SPark CLee C(2019)Multitask Pointer Network for Korean Dependency ParsingACM Transactions on Asian and Low-Resource Language Information Processing10.1145/328244218:3(1-10)Online publication date: 8-Feb-2019
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Zhao LQiu XZhang QHuang X(2019)Sequence Labeling With Deep Gated Dual Path CNNIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2019.294456327:12(2326-2335)Online publication date: 26-Nov-2019
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