Projective dependency parsing with perceptron

Xavier Carreras; Mihai Surdeanu; Lluís Màrquez

Projective dependency parsing with perceptron

Xavier Carreras, Mihai Surdeanu, Lluís Màrquez

Research output: Contribution to conference › Paper › peer-review

Abstract

We describe an online learning dependency parser for the CoNLL-X Shared Task, based on the bottom-up projective algorithm of Eisner (2000). We experiment with a large feature set that models: the tokens involved in dependencies and their immediate context, the surface-text distance between tokens, and the syntactic context dominated by each dependency. In experiments, the treatment of multilingual information was totally blind.

Original language	English (US)
Pages	181-185
Number of pages	5
State	Published - 2006
Externally published	Yes
Event	10th Conference on Computational Natural Language Learning, CoNLL 2006 - New York City, United States Duration: Jun 8 2006 → Jun 9 2006

Conference

Conference	10th Conference on Computational Natural Language Learning, CoNLL 2006
Country/Territory	United States
City	New York City
Period	6/8/06 → 6/9/06

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Linguistics and Language

Cite this

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title = "Projective dependency parsing with perceptron",

abstract = "We describe an online learning dependency parser for the CoNLL-X Shared Task, based on the bottom-up projective algorithm of Eisner (2000). We experiment with a large feature set that models: the tokens involved in dependencies and their immediate context, the surface-text distance between tokens, and the syntactic context dominated by each dependency. In experiments, the treatment of multilingual information was totally blind.",

author = "Xavier Carreras and Mihai Surdeanu and Llu{\'i}s M{\`a}rquez",

note = "Funding Information: This work was partially funded by the European Union Commission (PASCAL - IST-2002-506778) and Spanish Ministry of Science and Technology (TRANGRAM - TIN2004-07925-C03-02). Mihai Surdeanu was supported by a Ramon y Cajal fellowship of the latter institution. Publisher Copyright: {\textcopyright} 2006 Association for Computational Linguistics; 10th Conference on Computational Natural Language Learning, CoNLL 2006 ; Conference date: 08-06-2006 Through 09-06-2006",

year = "2006",

language = "English (US)",

pages = "181--185",

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T1 - Projective dependency parsing with perceptron

AU - Carreras, Xavier

AU - Surdeanu, Mihai

AU - Màrquez, Lluís

N1 - Funding Information: This work was partially funded by the European Union Commission (PASCAL - IST-2002-506778) and Spanish Ministry of Science and Technology (TRANGRAM - TIN2004-07925-C03-02). Mihai Surdeanu was supported by a Ramon y Cajal fellowship of the latter institution. Publisher Copyright: © 2006 Association for Computational Linguistics

PY - 2006

Y1 - 2006

N2 - We describe an online learning dependency parser for the CoNLL-X Shared Task, based on the bottom-up projective algorithm of Eisner (2000). We experiment with a large feature set that models: the tokens involved in dependencies and their immediate context, the surface-text distance between tokens, and the syntactic context dominated by each dependency. In experiments, the treatment of multilingual information was totally blind.

AB - We describe an online learning dependency parser for the CoNLL-X Shared Task, based on the bottom-up projective algorithm of Eisner (2000). We experiment with a large feature set that models: the tokens involved in dependencies and their immediate context, the surface-text distance between tokens, and the syntactic context dominated by each dependency. In experiments, the treatment of multilingual information was totally blind.

UR - http://www.scopus.com/inward/record.url?scp=85117733690&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85117733690&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85117733690

SP - 181

EP - 185

T2 - 10th Conference on Computational Natural Language Learning, CoNLL 2006

Y2 - 8 June 2006 through 9 June 2006

ER -

Projective dependency parsing with perceptron

Abstract

Conference

ASJC Scopus subject areas

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