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Unsupervised discovery of morphologically related words based on orthographic and semantic similarity

Authors:

Johannes Matiasek,

Harald TrostAuthors Info & Claims

MPL '02: Proceedings of the ACL-02 workshop on Morphological and phonological learning - Volume 6

Pages 48 - 57

https://doi.org/10.3115/1118647.1118653

Published: 11 July 2002 Publication History

Abstract

We present an algorithm that takes an unannotated corpus as its input, and returns a ranked list of probable morphologically related pairs as its output. The algorithm tries to discover morphologically related pairs by looking for pairs that are both orthographically and semantically similar, where orthographic similarity is measured in terms of minimum edit distance, and semantic similarity is measured in terms of mutual information. The procedure does not rely on a morpheme concatenation model, nor on distributional properties of word substrings (such as affix frequency). Experiments with German and English input give encouraging results, both in terms of precision (proportion of good pairs found at various cutoff points of the ranked list), and in terms of a qualitative analysis of the types of morphological patterns discovered by the algorithm.

References

[1]

A. Albright and B. Hayes. 1999. An automated learner for phonology and morphology. UCLA manuscript.

[2]

M. Baroni. 2000. Distributional cues in morpheme discovery: A computational model and empirical evidence. Ph.D. dissertation, UCLA.

[3]

M. Baroni, J. Matiasek and H. Trost. 2002. Wordform-and class-based prediction of the components of German nominal compounds in an AAC system. To appear in Proceedings of COLING 2002.

Digital Library

[4]

P. Brown, P. Della Pietra, P. DeSouza, J. Lai, and R. Mercer. 1990. Class-based n-gram models of natural language. Computational Linguistics, 18:467--479.

Digital Library

[5]

K. Church and P. Hanks. 1989. Word association norms, mutual information, and lexicography. Proceedings of ACL 27, 76--83.

Digital Library

[6]

J. Goldsmith. 2001. Unsupervised learning of the morphology of a natural language. Computational Linguistics, 27:153--198.

Digital Library

[7]

C. Jacquemin. 1997. Guessing morphology from terms and corpora. Proceedings of SIGIR 97, 156--265.

Digital Library

[8]

D. Jurafsky and J. Martin. 2000. Speech and Language Processing. Prentice-Hall, Upper Saddle River, NJ.

Digital Library

[9]

L. Karttunen, K. Gaál, and A. Kempe. 1997. Xerox Finite-State Tool Xerox Research Centre Europe, Grenoble.

[10]

H. Kučera and N. Francis. 1967. Computational analysis of present-day American English. Brown University Press, Providence, RI.

[11]

C. Manning and H. Schütze. 1999. Foundations of statistical natural language processing. MIT Press, Cambridge, MASS.

Digital Library

[12]

S. Neuvel. 2002. Whole word morphologizer. Expanding the word-based lexicon: A non-stochastic computational approach. Brain and Language, in press.

[13]

R. Rosenfeld. 1996. A maximum entropy approach to adaptive statistical language modeling. Computer Speech and Language, 10:187--228.

[14]

P. Schone and D. Jurafsky. 2000. Knowldedge-free induction of morphology using latent semantic analysis. Proceedings of the Conference on Computational Natural Language Learning.

Digital Library

[15]

M. Snover and M. Brent. 2001. A Bayesian model for morpheme and paradigm identification. Proceedings of ACL 39, 482-490.

Digital Library

[16]

D. Yarowksy and R. Wicentowski. 2000. Minimally supervised morphological analysis by multimodal alignment. Proceedings of ACL 38, 207--216.

Digital Library

Cited By

Paik JParui SPal DRobertson S(2013)Effective and Robust Query-Based StemmingACM Transactions on Information Systems10.1145/2536736.253673831:4(1-29)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1145/2536736.2536738
Cohen TWiddows DWahle MSchvaneveldt R(2013)Orthogonality and OrthographySelected Papers of the 7th International Conference on Quantum Interaction - Volume 836910.1007/978-3-642-54943-4_4(34-46)Online publication date: 25-Jul-2013
https://dl.acm.org/doi/10.1007/978-3-642-54943-4_4
Kirschenbaum A(2013)Unsupervised segmentation for different types of morphological processes using multiple sequence alignmentProceedings of the First international conference on Statistical Language and Speech Processing10.1007/978-3-642-39593-2_14(152-163)Online publication date: 29-Jul-2013
https://dl.acm.org/doi/10.1007/978-3-642-39593-2_14
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Unsupervised discovery of morphologically related words based on orthographic and semantic similarity
1. Computing methodologies
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  1. Power and energy
    1. Power estimation and optimization

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cover image DL Hosted proceedings

MPL '02: Proceedings of the ACL-02 workshop on Morphological and phonological learning - Volume 6

July 2002

82 pages

Program Chair:
Mike Maxwell
Linguistic Data Consortium, University of Pennsylvania

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 11 July 2002

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Cited By

Paik JParui SPal DRobertson S(2013)Effective and Robust Query-Based StemmingACM Transactions on Information Systems10.1145/2536736.253673831:4(1-29)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1145/2536736.2536738
Cohen TWiddows DWahle MSchvaneveldt R(2013)Orthogonality and OrthographySelected Papers of the 7th International Conference on Quantum Interaction - Volume 836910.1007/978-3-642-54943-4_4(34-46)Online publication date: 25-Jul-2013
https://dl.acm.org/doi/10.1007/978-3-642-54943-4_4
Kirschenbaum A(2013)Unsupervised segmentation for different types of morphological processes using multiple sequence alignmentProceedings of the First international conference on Statistical Language and Speech Processing10.1007/978-3-642-39593-2_14(152-163)Online publication date: 29-Jul-2013
https://dl.acm.org/doi/10.1007/978-3-642-39593-2_14
Darwish KAli ALi HLin COsborne M(2012)Arabic retrieval revisitedProceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 210.5555/2390665.2390719(218-222)Online publication date: 8-Jul-2012
https://dl.acm.org/doi/10.5555/2390665.2390719
Dreyer MEisner JMerlo PBarzilay RJohnson M(2011)Discovering morphological paradigms from plain text using a Dirichlet process mixture modelProceedings of the Conference on Empirical Methods in Natural Language Processing10.5555/2145432.2145504(616-627)Online publication date: 27-Jul-2011
https://dl.acm.org/doi/10.5555/2145432.2145504
Bergsma SBhargava AHe HKondrak GLi HMàrquez L(2010)Predicting the semantic compositionality of prefix verbsProceedings of the 2010 Conference on Empirical Methods in Natural Language Processing10.5555/1870658.1870687(293-303)Online publication date: 9-Oct-2010
https://dl.acm.org/doi/10.5555/1870658.1870687
Moon TErk KBaldridge JKoehn PMihalcea R(2009)Unsupervised morphological segmentation and clustering with document boundariesProceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 210.5555/1699571.1699600(668-677)Online publication date: 6-Aug-2009
https://dl.acm.org/doi/10.5555/1699571.1699600
Kohonen OVirpioja SKlami M(2008)AllomorfessorProceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access10.5555/1813809.1813959(975-982)Online publication date: 17-Sep-2008
https://dl.acm.org/doi/10.5555/1813809.1813959
Hathout NMatveeva IBiemann CChoudhury MDiab M(2008)Acquistion of the morphological structure of the lexicon based on lexical similarity and formal analogyProceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing10.5555/1627328.1627329(1-8)Online publication date: 24-Aug-2008
https://dl.acm.org/doi/10.5555/1627328.1627329
Gelbukh ASidorov GLara-Reyes DChanona-Hernandez L(2008)Division of Spanish Words into Morphemes with a Genetic AlgorithmProceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems10.1007/978-3-540-69858-6_4(19-26)Online publication date: 24-Jun-2008
https://dl.acm.org/doi/10.1007/978-3-540-69858-6_4
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