Probabilistic Hierarchical Clustering Of Morphological Paradigms

Suresh Kumar Manandhar; Burcu Can

Probabilistic Hierarchical Clustering Of Morphological Paradigms

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We propose a novel method for learning morphological paradigms that are structured within a hierarchy. The hierarchical structuring of paradigms groups morphologically similar words close to each other in a tree structure. This allows detecting morphological similarities easily leading to improved morphological segmentation. Our evaluation using (Kurimo et al., 2011a; Kurimo et al., 2011b) dataset shows that our method performs competitively when compared with current state-of- art systems.

Original language	English
Title of host publication	EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Place of Publication	Stroudsburg, PA
Publisher	Association for Computational Linguistics
Pages	654-663
Number of pages	10
ISBN (Print)	978-1-937284-19-0
Publication status	Published - 23 Apr 2012
Event	European Chapter of the Association for Computational Linguistics (EACL) - Avignon, France Duration: 23 Apr 2012 → 27 Nov 2012

Conference

Conference	European Chapter of the Association for Computational Linguistics (EACL)
Country/Territory	France
City	Avignon
Period	23/04/12 → 27/11/12

Access to Document

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Cite this

@inproceedings{415506ad1cf64e05921b86c1435edeb9,

title = "Probabilistic Hierarchical Clustering Of Morphological Paradigms",

abstract = "We propose a novel method for learning morphological paradigms that are structured within a hierarchy. The hierarchical structuring of paradigms groups morphologically similar words close to each other in a tree structure. This allows detecting morphological similarities easily leading to improved morphological segmentation. Our evaluation using (Kurimo et al., 2011a; Kurimo et al., 2011b) dataset shows that our method performs competitively when compared with current state-of- art systems.",

author = "Manandhar, {Suresh Kumar} and Burcu Can",

year = "2012",

month = apr,

day = "23",

language = "English",

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pages = "654--663",

booktitle = "EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics",

note = "European Chapter of the Association for Computational Linguistics (EACL) ; Conference date: 23-04-2012 Through 27-11-2012",

}

Manandhar, SK & Can, B 2012, Probabilistic Hierarchical Clustering Of Morphological Paradigms. in EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics . Association for Computational Linguistics, Stroudsburg, PA, pp. 654-663, European Chapter of the Association for Computational Linguistics (EACL), Avignon, France, 23/04/12.

Probabilistic Hierarchical Clustering Of Morphological Paradigms. / Manandhar, Suresh Kumar; Can, Burcu.
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics . Stroudsburg, PA: Association for Computational Linguistics, 2012. p. 654-663.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Can, Burcu

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N2 - We propose a novel method for learning morphological paradigms that are structured within a hierarchy. The hierarchical structuring of paradigms groups morphologically similar words close to each other in a tree structure. This allows detecting morphological similarities easily leading to improved morphological segmentation. Our evaluation using (Kurimo et al., 2011a; Kurimo et al., 2011b) dataset shows that our method performs competitively when compared with current state-of- art systems.

AB - We propose a novel method for learning morphological paradigms that are structured within a hierarchy. The hierarchical structuring of paradigms groups morphologically similar words close to each other in a tree structure. This allows detecting morphological similarities easily leading to improved morphological segmentation. Our evaluation using (Kurimo et al., 2011a; Kurimo et al., 2011b) dataset shows that our method performs competitively when compared with current state-of- art systems.

M3 - Conference contribution

SN - 978-1-937284-19-0

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BT - EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

PB - Association for Computational Linguistics

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