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Robust, applied morphological generation

Authors:

Darren PearceAuthors Info & Claims

INLG '00: Proceedings of the first international conference on Natural language generation - Volume 14

Pages 201 - 208

https://doi.org/10.3115/1118253.1118281

Published: 12 June 2000 Publication History

Abstract

In practical natural language generation systems it is often advantageous to have a separate component that deals purely with morphological processing. We present such a component: a fast and robust morphological generator for English based on finite-state techniques that generates a word form given a specification of the lemma, part-of-speech, and the type of inflection required. We describe how this morphological generator is used in a prototype system for automatic simplification of English newspaper text, and discuss practical morphological and orthographic issues we have encountered in generation of unrestricted text within this application.

References

[1]

Alfred Aho, Ravi Sethi, and Jeffrey Ullman. 1986. Compilers, Principles, Techniques and Tools. Addison-Wesley.

Digital Library

[2]

Hiyan Alshawi, editor. 1992. The Core Language Engine. MIT Press, Cambridge, MA.

[3]

Harald Baayen, Richard Piepenbrock, and Hed-derik van Rijn. 1993. The CELEX Lexical Database (CD-ROM). Linguistic Data Consortium, University of Pennsylvania, Philadelphia, PA, USA.

[4]

John Bateman. 2000. KPML (Version 3.1) March 2000. University of Bremen, Germany, <http://www.fb10.uni-bremen.de/anglistik/langpro/kpml/README.html>.

[5]

Lou Burnard. 1995. Users reference guide for the British National Corpus. Technical report, Oxford University Computing Services.

[6]

Lynne Cahill. 1993. Morphonology in the lexicon. In Proceedings of the 6th Conference of the European Chapter of the Association for Computational Linguistics, pages 87--96, Utrecht, The Netherlands.

Digital Library

[7]

Yvonne Canning and John Tait. 1999. Syntactic simplification of newspaper text for aphasic readers. In Proceedings of the ACM SIGIR Workshop on Customised Information Delivery, Berkeley, CA, USA.

[8]

John Carroll, Guido Minnen, Darren Pearce, Yvonne Canning, Siobhan Devlin, and John Tait. 1999. Simplifying English text for language impaired readers. In Proceedings of the 9th Conference of the European Chapter of the Association for Computational Linguistics (EACL), Bergen, Norway.

[9]

Hamish Cunningham, Yorick Wilks, and Robert Gaizauskas. 1996. GATE---a General Architecture for Text Engineering. In Proceedings of the 16th Conference on Computational Linguistics, Copenhagen, Denmark.

Digital Library

[10]

Siobhan Devlin and John Tait. 1998. The use of a psycholinguistic database in the simplification of text for aphasic readers. In (Nerbonne. 1998).

[11]

Michael Elhadad and Jacques Robin. 1996. An overview of SURGE: A reusable comprehensive syntactic realization component. Technical Report 96-03, Dept of Mathematics and Computer Science, Ben Gurion University, Israel.

[12]

Roger Evans and Gerald Gazdar. 1996. DATR: a language for lexical knowledge representation. Computational Linguistics, 22.

Digital Library

[13]

Roger Garside, Geoffrey Leech, and Geoffrey Sampson. 1987. The computational analysis of English: a corpus-based approach. Longman, London.

[14]

Lauri Karttunen, Jean-Pierre Chanod, Gregory Grefenstette, and Anne Schiller. 1996. Regular expressions for language engineering. Natural Language Engineering, 2(4):305--329.

Digital Library

[15]

Lauri Karttunen. 1994. Constructing lexical transducers. In Proceedings of the 14th Conference on Computational Linguistics, pages 406--411, Kyoto, Japan.

Digital Library

[16]

Kimmo Koskenniemi. 1983. Two-level model for morphological analysis. In 8th International Joint Conference on Artificial Intelligence, pages 683--685, Karlsruhe, Germany.

[17]

John Levine, Tony Mason, and Doug Brown. 1992. Lex & Yacc. O'Reilly and Associates, second edition.

[18]

Mitch Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics, 19(2):313--330.

Digital Library

[19]

Christian Matthiessen. 1984. Systemic Grammar in computation: The Nigel case. In Proceedings of the 1st Conference of the European Chapter of the Association for Computational Linguistics, pages 155--164, Pisa, Italy.

Digital Library

[20]

George Miller, Richard Beckwith, Christiane Fellbaum, Derek Gross, Katherine Miller, and Randee Tengi. 1993. Five Papers on WordNet. Princeton University, Princeton, N.J.

[21]

Guido Minnen and John Carroll. Under review. Fast and robust morphological processing tools for practical NLP applications.

[22]

Roger Mitton. 1992. A description of a computer-usable dictionary file based on the Oxford Advanced Learner's Dictionary of Current English. Available at <ftp://ota.ox.ac.uk/pub/ota/public/dicts/710/text710.doc>.

[23]

Mehryar Mohri. 1996. On some applications of finite state automata theory to natural language processing. Natural Language Engineering, 2(1):61--80.

Digital Library

[24]

John Nerbonne, editor. 1998. Linguistic Databases. Lecture Notes. CSLI Publications, Stanford, USA.

[25]

Richard Power, Donia Scott, and Roger Evans. 1998. What You See Is What You Meant: direct knowledge editing with natural language feedback. In Proceedings of the 13th Biennial European Conference on Artificial Intelligence (ECAI 98), Brighton, UK.

[26]

Paul Procter. 1995. Cambridge International Dictionary of English. Cambridge University Press.

[27]

Geoffrey Pullum and Arnold Zwicky. In preparation. Licensing of prosodic features by syntactic rules: the key to auxiliary reduction. First version presented to the Annual Meeting of the Linguistic Society of America, Chicago, Illinois, January 1997. Available at <http://www.lsadc.org/web2/99modabform.htm>

[28]

Philip Quinlan. 1992. The Oxford Psycholinguistic Database. Oxford University Press.

[29]

Graeme Ritchie, Graham Russell, Alan Black, and Stephen Pulman. 1992. Computational morphology: practical mechanisms for the English lexicon. MIT Press.

[30]

Geoffrey Sampson. 1995. English for the computer. Oxford University Press.

[31]

Stuart Shieber, Gertjan van Noord, Robert Moore, and Fernando Pereira. 1990. Semantic head-driven generation. Computational Linguistics, 16(1):7--17.

Digital Library

[32]

Lita Taylor and Gerry Knowles. 1988. Manual of information to accompany the SEC Corpus: the machine-readable corpus of spoken English. Manuscript, University of Lancaster, UK.

[33]

Gertjan van Noord. 1991. Morphology in MiMo2. Manuscript, University of Utrecht, The Netherlands.

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cover image DL Hosted proceedings

INLG '00: Proceedings of the first international conference on Natural language generation - Volume 14

June 2000

288 pages

ISBN:9659029608

Conference Chair:
Michael Elhadad
Ben Gurion University, Israel

Sponsors

The Association for Computational Linguistics

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 12 June 2000

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Cited By

Premjith BSoman K(2021)Deep Learning Approach for the Morphological Synthesis in Malayalam and Tamil at the Character LevelACM Transactions on Asian and Low-Resource Language Information Processing10.1145/345797620:6(1-17)Online publication date: 12-Aug-2021
https://dl.acm.org/doi/10.1145/3457976
Kjersten BVan Durme BChu-Carroll J(2012)Space efficiencies in discourse modeling via conditional random samplingProceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies10.5555/2382029.2382103(513-517)Online publication date: 3-Jun-2012
https://dl.acm.org/doi/10.5555/2382029.2382103
Girju RPaul M(2011)Modeling reciprocity in social interactions with probabilistic latent space modelsNatural Language Engineering10.1017/S135132491000017317:1(1-36)Online publication date: 1-Jan-2011
https://dl.acm.org/doi/10.1017/S1351324910000173
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Grover CTobin RAlex BByrne KErk KStrapparava C(2010)Edinburgh-LTG: TempEval-2 system descriptionProceedings of the 5th International Workshop on Semantic Evaluation10.5555/1859664.1859738(333-336)Online publication date: 15-Jul-2010
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Rimell LClark SErk KStrapparava C(2010)Cambridge: Parser evaluation using textual entailment by grammatical relation comparisonProceedings of the 5th International Workshop on Semantic Evaluation10.5555/1859664.1859724(268-271)Online publication date: 15-Jul-2010
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Tata SDi Eugenio BHajič J(2010)Generating fine-grained reviews of songs from album reviewsProceedings of the 48th Annual Meeting of the Association for Computational Linguistics10.5555/1858681.1858821(1376-1385)Online publication date: 11-Jul-2010
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Nahnsen TGermann UShah CStoyanchev S(2009)Domain-independent shallow sentence orderingProceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium10.5555/1620932.1620946(78-83)Online publication date: 1-Jun-2009
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Žabokrtský ZPtáček JPajas PCallison-Burch CKoehn PMonz CSchroeder JFordyce C(2008)TectoMTProceedings of the Third Workshop on Statistical Machine Translation10.5555/1626394.1626419(167-170)Online publication date: 19-Jun-2008
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Haddow BDemner-Fushman DAnaniadou SCohen KPestian JTsujii JWebber B(2008)Using automated feature optimisation to create an adaptable relation extraction systemProceedings of the Workshop on Current Trends in Biomedical Natural Language Processing10.5555/1572306.1572310(19-27)Online publication date: 19-Jun-2008
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