Learning Multilingual Morphology with CLOG

Suresh Manandhar; Saso Dzeroski; Tomaz Erjavec

Learning Multilingual Morphology with CLOG

Suresh Manandhar, Saso Dzeroski, Tomaz Erjavec

Computer Science

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

Abstract

The paper presents the decision list learning system Clog and the results of using it to learn nominal inflections of English, Romanian, Czech, Slovene, and Estonian. The dataset used to induce rules for the synthesis and analysis of the inflectional paradigms of nouns and adjectives of these languages is the Multext-East multilingual tagged corpus. The ILP system Foidl is also applied to the same dataset, and this paper compares the induction methodology and results of the two systems. The experiment shows that the accuracy of the two systems is comparable when using the same training set. However, while Foidl is, due to efficiency reasons, severely limited in the size of the training set, Clog does not suffer from such limitations. With the increase of the training set size possible with Clog, it significantly outperforms Foidl and learns highly accurate morphological rules. 1 Introduction Machine learning methods been recently applied to a variety of tasks within the area of n...

Original language	Undefined/Unknown
Pages	135-144
Publication status	Published - 1998

Access to Document

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.4276

Cite this

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title = "Learning Multilingual Morphology with CLOG",

abstract = "The paper presents the decision list learning system Clog and the results of using it to learn nominal inflections of English, Romanian, Czech, Slovene, and Estonian. The dataset used to induce rules for the synthesis and analysis of the inflectional paradigms of nouns and adjectives of these languages is the Multext-East multilingual tagged corpus. The ILP system Foidl is also applied to the same dataset, and this paper compares the induction methodology and results of the two systems. The experiment shows that the accuracy of the two systems is comparable when using the same training set. However, while Foidl is, due to efficiency reasons, severely limited in the size of the training set, Clog does not suffer from such limitations. With the increase of the training set size possible with Clog, it significantly outperforms Foidl and learns highly accurate morphological rules. 1 Introduction Machine learning methods been recently applied to a variety of tasks within the area of n...",

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AU - Dzeroski, Saso

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AB - The paper presents the decision list learning system Clog and the results of using it to learn nominal inflections of English, Romanian, Czech, Slovene, and Estonian. The dataset used to induce rules for the synthesis and analysis of the inflectional paradigms of nouns and adjectives of these languages is the Multext-East multilingual tagged corpus. The ILP system Foidl is also applied to the same dataset, and this paper compares the induction methodology and results of the two systems. The experiment shows that the accuracy of the two systems is comparable when using the same training set. However, while Foidl is, due to efficiency reasons, severely limited in the size of the training set, Clog does not suffer from such limitations. With the increase of the training set size possible with Clog, it significantly outperforms Foidl and learns highly accurate morphological rules. 1 Introduction Machine learning methods been recently applied to a variety of tasks within the area of n...

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