An informal discovery procedure for two-level rulet

• Autorzy: Koskenniemi K.

Identyfikatory

DOI

10.15398/jlm.v1i1.62

• Języki publikacji: EN

Abstrakty

EN

The paper shows how a certain kind of underlying representations (or deep forms) of words can be constructed in a straightforward manner through aligning the surface forms of the morphs of the word forms. The inventory of morphophonemes follows directly from this alignment. Furthermore, the two-level rules which govern the different realisations of such morphophonemes follow fairly directly from the previous steps. The alignment and rules are based upon an approximate general metric among phonemes, e.g., articulatory features, that determines which alternations are likely or possible. This enables us to summarise contexts for the different realisations.

Słowa kluczowe

EN

morphological analysis; discovery procedure; two-level rules; two-level morphology; IPA

• Wydawca: Instytut Podstaw Informatyki PAN
• Czasopismo: Journal of Language Modelling
• Rocznik: 2013
• Tom: Vol. 1, No. 1
• Strony: 155--188
• Opis fizyczny: Bibliogr. 26 poz., tab.

Twórcy

autor

Koskenniemi K.

• Department of Modern Languages, University of Helsinki, Finland

Bibliografia

• [1] Kenneth R. Beesley and Lauri Karttunen (2003), Finite State Morphology, Studies in Computational Linguistics, 3, University of Chicago Press, additional info, see: www.stanford.edu/~laurik/fsmbook/home.html.
• [2] Erwin Chan (2008), Structures and Distributions in Morphology Learning, a dissertation in Computer and Information Science, University of Pennsylvania.
• [3] Mathias Creutz and Krista Lagus (2004), Induction of a Simple Morphology for Highly-Inflecting Languages, in Proceedings of the Seventh Meeting of the ACL Special Interest Group in Computational Phonology, pp. 43-51, Association for Computational Linguistics, Stroudsburg, PA, USA.
• [4] Daniel Gildea and Daniel Jurafsky (1995), Automatic induction of finie state transducers for simple phonological rules, in Proceedings of the 33rd annual meeting on Association for Computational Linguistics, pp. 9-15, Association for Computational Linguistics, Cambridge, Massachusetts.
• [5] John Goldsmith (2006), An Algorithm for the Unsupervised Learning of Morphology, Natural Language Engineering, 12 (4): 353-371.
• [6] Sharon Goldwater and Mark Johnson (2004), Priors in Bayesian Learning of Phonological Rules, in Proceedings of the Seventh Meeting of the ACL Special Interest Group in Computational Phonology, pp. 35-42, Association for Computational Linguistics, Stroudsburg, PA, USA.
• [7] Mans Hulden (2009), Foma: a Finite-State Compiler and Library, in Proceedings of the Demonstrations Session at EACL 2009, pp. 29-32, Association for Computational Linguistics, Stroudsburg, PA, USA, http://www.aclweb.org/anthology/E09-2008.
• [8] Mans Hulden, Iñaki Alegria, Izaskun Etxeberria, and Montse Maritxalar (2011), Learning word-level dialectal variation as phonological replacement rules using a limited parallel corpus, in Proceedings of EMNLP 2011, Conference on Empirical Methods in Natural Language Processing, DIALECTS’11, Association for Computational Linguistics, Stroudsburg, PA, USA.
• [9] Mark Johnson (1984), A Discovery Procedure for Certain Phonological Rules, in Proceedings of the 10th International Conference on Computational Linguistics and 22nd Annual Meeting of the Association for Computational Linguistics, pp. 344-347, Association for Computational Linguistics, Stroudsburg, PA, USA, http://www.aclweb.org/anthology/P84-1070.
• [10] Lauri Karttunen (1993), Finite-state Constraints, in Proceedings of the International Conference on Current Issues in Computational Linguistics, June 10-14, 1991. Universiti Sains Malaysia, Penang, Malaysia, pp. 173-194.
• [11] Lauri Karttunen and Kenneth R. Beesley (2001), A short history of two-level morphology, http://www.helsinki.fi/esslli/evening/20years/twol-history.pdf.
• [12] Lauri Karttunen, Kimmo Koskenniemi, and Ronald M. Kaplan (1987), A compiler for two-level phonological rules, in M. Dalrymple, R. Kaplan, L. Karttunen, K. Koskenniemi, S. Shaio, and M. Wescoat, editors, Tools for Morphological Analysis, volume 87-108 of CSLI Reports, pp. 1-61, Center for the Study of Language and Information, Stanford University, Palo Alto, California, USA.
• [13] Laura Kataja and Kimmo Koskenniemi (1988), Finite-state Description of Semitic Morphology: A Case Study of Ancient Accadian, in COLING Budapest: Proceedings of the 12th Conference on Computational Linguistics, pp. 313-315, Association for Computational Linguistics, Stroudsburg, PA, USA, http://aclweb.org/anthology-new/C/C88/C88-1064.pdf.
• [14] Grzegorz Kondrak (2002), Algorithms for Language Reconstruction, Ph.D. thesis, University of Toronto.
• [15] Kimmo Koskenniemi (1983), Two-level Morphology: A General Computational Model for Word-Form Recognition and Production, number 11 in Publications, University of Helsinki, Department of General Linguistics.
• [16] Kimmo Koskenniemi (1984), A General Computational Model for Word-Form Recognition and Production, in Proceedings of COLING-84, 2-4 July 1984, Stanford University, California, pp. 178-181, Association for Computational Linguistics, Stroudsburg, PA, USA.
• [17] Kimmo Koskenniemi (1991), A Discovery Procedure for Two-level Phonology, in L. Cignoni and C. Peters, editors, Computational Lexicology and Lexicography: Special Issue Dedicated to Bernard Quemada, volume VI:I, Giardini editori e stampatori in Pisa, Pisa, Italy.
• [18] Krister Lindén, Erik Axelson, Sam Hardwick, Tommi A. Pirinen, and Miikka Silfverberg (2011), HFST – Framework for Compiling and Applying Morphologies, in C. Mahlow and M. Piotrowski, editors, Systems and Frameworks for Computational Morphology 2011 (SFCM-2011), volume 100 of Communications in Computer and Information Science, pp. 67-85, Springer-Verlag.
• [19] Tom M. Mitchell (1982), Generalization as search, Artificial Intelligence, 18 (2): 203-226.
• [20] Kemal Oflazer, Sergei Nirenburg, and Marjorie McShane (2001), Bootstrapping morphological analyzers by combining human elicitation and machine learning, Computational Linguistics, 27 (1): 59-85.
• [21] Kemal Oflazer and Sergei Nirenburg (1999), Practical Bootstrapping of Morphological Analyzers, in Proceedings of Computational Natural Language Learning (CoNLL99). Workshop at EACL’99, pp. 143-146, Springer-Verlag.
• [22] José Oncina, Pedro García, and Enrique Vidal (1993), Learning subsequential transducers for pattern recognition interpretation tasks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15 (5): 448-458.
• [23] Miikka Silfverberg and Krister Lindén (2009), Conflict Resolution Using Weighted Rules in HFST-TWOLC, in Proceedings of the 17th Nordic Conference of Computational Linguistics, NODALIDA 2009, pp. 174-181, Northern European Association for Language Technology (NEALT), http://hdl.handle.net/10062/9752.
• [24] Pieter Theron and Ian Cloete (1997), Automatic Acquisition of Two-Level Morphological Rules, in Fifth Conference on Applied Natural Language Processing Proceedings of the Conference, pp. 103-110, Association for Computational Linguistics.
• [25] David Touretzky, Gillette Elvgren, and Deirdre W. Wheeler (1990), Phonological rule induction: An architectural solution, in Proceedings of the 12th Annual Conference of the Cognitive Science Society (COGSCI-90), pp. 348-355, Cognitive Science Society.
• [26] David Yarowsky and Richard Wicentowski (2000), Minimally supervised morphological analysis by multimodal alignment, in Proceedings of the 38th Annual Meeting on Association for Computational Linguistics: Hong Kong, Association for Computational Linguistics.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).