Morphological disambiguation of Hebrew: A case study in classifier combination

Danny Shacham; Shuly Wintner

Morphological disambiguation of Hebrew: A case study in classifier combination

Danny Shacham
, Shuly Wintner

Department of Computer Science

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

Abstract

Morphological analysis and disambiguation are crucial stages in a variety of natural language processing applications, especially when languages with complex morphology are concerned. We present a system which disambiguates the output of a morphological analyzer for Hebrew. It consists of several simple classifiers and a module which combines them under linguistically motivated constraints. We investigate a number of techniques for combining the predictions of the classifiers. Our best result, 91.44% accuracy, reflects a 25% reduction in error rate compared with the previous state of the art.

Original language	English
Pages	439-447
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: 28 Jun 2007 → 28 Jun 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	28/06/07 → 28/06/07

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Information Systems

Cite this

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title = "Morphological disambiguation of Hebrew: A case study in classifier combination",

abstract = "Morphological analysis and disambiguation are crucial stages in a variety of natural language processing applications, especially when languages with complex morphology are concerned. We present a system which disambiguates the output of a morphological analyzer for Hebrew. It consists of several simple classifiers and a module which combines them under linguistically motivated constraints. We investigate a number of techniques for combining the predictions of the classifiers. Our best result, 91.44\% accuracy, reflects a 25\% reduction in error rate compared with the previous state of the art.",

author = "Danny Shacham and Shuly Wintner",

year = "2007",

language = "English",

pages = "439--447",

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",

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Morphological disambiguation of Hebrew: A case study in classifier combination. / Shacham, Danny; Wintner, Shuly.
2007. 439-447 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 - Morphological disambiguation of Hebrew

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

AU - Shacham, Danny

AU - Wintner, Shuly

PY - 2007

Y1 - 2007

N2 - Morphological analysis and disambiguation are crucial stages in a variety of natural language processing applications, especially when languages with complex morphology are concerned. We present a system which disambiguates the output of a morphological analyzer for Hebrew. It consists of several simple classifiers and a module which combines them under linguistically motivated constraints. We investigate a number of techniques for combining the predictions of the classifiers. Our best result, 91.44% accuracy, reflects a 25% reduction in error rate compared with the previous state of the art.

AB - Morphological analysis and disambiguation are crucial stages in a variety of natural language processing applications, especially when languages with complex morphology are concerned. We present a system which disambiguates the output of a morphological analyzer for Hebrew. It consists of several simple classifiers and a module which combines them under linguistically motivated constraints. We investigate a number of techniques for combining the predictions of the classifiers. Our best result, 91.44% accuracy, reflects a 25% reduction in error rate compared with the previous state of the art.

UR - https://www.scopus.com/pages/publications/80053346014

M3 - Paper

AN - SCOPUS:80053346014

SP - 439

EP - 447

Y2 - 28 June 2007 through 28 June 2007

ER -

Morphological disambiguation of Hebrew: A case study in classifier combination

Abstract

Conference

ASJC Scopus subject areas

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