Determining case in Arabic: Learning complex linguistic behavior requires complex linguistic features

Nizar Habash; Ryan Gabbard; Owen Rambow; Seth Kulick; Mitch Marcus

Determining case in Arabic: Learning complex linguistic behavior requires complex linguistic features

Nizar Habash
, Ryan Gabbard
, Owen Rambow
, Seth Kulick
, Mitch Marcus

Linguistics

Columbia University
University of Pennsylvania

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

30 Scopus citations

Abstract

This paper discusses automatic determination of case in Arabic. This task is a major source of errors in full diacritization of Arabic. We use a gold-standard syntactic tree, and obtain an error rate of about 4.2%, with a machine learning based system outperforming a system using hand-written rules. A careful error analysis suggests that when we account for annotation errors in the gold standard, the error rate drops to 0.8%, with the hand-written rules outperforming the machine learning-based system.

Original language	English
Pages	1084-1092
Number of pages	9
State	Published - 2007
Event	2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007 - Prague, Czech Republic Duration: Jun 28 2007 → Jun 28 2007

Conference

Conference	2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007
Country/Territory	Czech Republic
City	Prague
Period	06/28/07 → 06/28/07

Cite this

@conference{2e64b7fe56174ef6b5df343271b066cd,

title = "Determining case in Arabic: Learning complex linguistic behavior requires complex linguistic features",

abstract = "This paper discusses automatic determination of case in Arabic. This task is a major source of errors in full diacritization of Arabic. We use a gold-standard syntactic tree, and obtain an error rate of about 4.2\%, with a machine learning based system outperforming a system using hand-written rules. A careful error analysis suggests that when we account for annotation errors in the gold standard, the error rate drops to 0.8\%, with the hand-written rules outperforming the machine learning-based system.",

author = "Nizar Habash and Ryan Gabbard and Owen Rambow and Seth Kulick and Mitch Marcus",

year = "2007",

language = "English",

pages = "1084--1092",

note = "2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007 ; Conference date: 28-06-2007 Through 28-06-2007",

}

Habash, N, Gabbard, R, Rambow, O, Kulick, S & Marcus, M 2007, 'Determining case in Arabic: Learning complex linguistic behavior requires complex linguistic features', Paper presented at 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007, Prague, Czech Republic, 06/28/07 - 06/28/07 pp. 1084-1092.

Determining case in Arabic: Learning complex linguistic behavior requires complex linguistic features. / Habash, Nizar; Gabbard, Ryan; Rambow, Owen et al.
2007. 1084-1092 Paper presented at 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007, Prague, Czech Republic.

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

TY - CONF

T1 - Determining case in Arabic

T2 - 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2007

AU - Habash, Nizar

AU - Gabbard, Ryan

AU - Rambow, Owen

AU - Kulick, Seth

AU - Marcus, Mitch

PY - 2007

Y1 - 2007

N2 - This paper discusses automatic determination of case in Arabic. This task is a major source of errors in full diacritization of Arabic. We use a gold-standard syntactic tree, and obtain an error rate of about 4.2%, with a machine learning based system outperforming a system using hand-written rules. A careful error analysis suggests that when we account for annotation errors in the gold standard, the error rate drops to 0.8%, with the hand-written rules outperforming the machine learning-based system.

AB - This paper discusses automatic determination of case in Arabic. This task is a major source of errors in full diacritization of Arabic. We use a gold-standard syntactic tree, and obtain an error rate of about 4.2%, with a machine learning based system outperforming a system using hand-written rules. A careful error analysis suggests that when we account for annotation errors in the gold standard, the error rate drops to 0.8%, with the hand-written rules outperforming the machine learning-based system.

UR - https://www.scopus.com/pages/publications/55649104405

M3 - Paper

AN - SCOPUS:55649104405

SP - 1084

EP - 1092

Y2 - 28 June 2007 through 28 June 2007

ER -

Determining case in Arabic: Learning complex linguistic behavior requires complex linguistic features

Abstract

Conference

Other files and links

Fingerprint

Cite this