Article

Building an inflectional stemmer for Bulgarian

Author:

Preslav NakovAuthors Info & Claims

CompSysTech '03: Proceedings of the 4th international conference conference on Computer systems and technologies: e-Learning

Pages 419 - 424

https://doi.org/10.1145/973620.973690

Published: 19 June 2003 Publication History

Abstract

The paper starts with an overview of the most important approaches to stemming for English as well as for some Slavic languages. Then, the design, implementation and evaluation of an inflectional stemmer for Bulgarian are described. The problem is addressed as a machine-learning task from a large morphological dictionary. A detailed automatic evaluation for different parameter values in terms of under-stemming, over-stemming and coverage is provided.

References

[1]

{1} Adamson G., J. Boreham. The use of an association measure based on character structure to identify semantically related pairs of words and document titles. In Information Processing & Management, vol. 10(7/8), pp. 253-260, 1974.

[2]

{2} Dawson J. 1974. Suffix removal for word conflation. In Bulletin of the Association for Literary & Linguistic Computing. vol. 2(3), pp. 33-46, 1974.

[3]

{3} Frakes W. Term Conflation for Information Retrieval. PhD. dissertation. Syracuse University, August 1982.

[4]

{4} Frakes W., R. Baeza-Yates. Information Retrieval: Data Structures & Algorithms. Englewood Cliffs, N J: Prentice-Hall, 1992. (Chapter 8).

Digital Library

[5]

{5} Hafer M., S. Weiss. Word segmentation by letter successor varieties. In Information Processing & Management, vol. 10(11/12), pp. 371-386, 1974.

[6]

{6} Harman, D. How effective is suffixing? In Journal of The American Society of Information Science. Vol. 42, No 1, pp. 7-15. 1991.

[7]

{7} Hull, D. Stemming Algorithms: A Case study for detailed evaluation. In Journal of The American Society of Information Science. Vol. 47, No 1. pp. 70-84. 1996.

Digital Library

[8]

{8} Kovalenko A. Stemka: Morphological analyzer for small search systems. In System Administrator Magazine. Moscow, October 2002.

[9]

{9} Kraaij W. R. Pohlmann. Porter's stemming algorithm for Dutch. In Noordman LGM and de Vroomen WAM, eds. Informatiewetenschap 1994: Wetenschappelijke bijdragen aan de derde STINFON Conferentie, Tilburg, pp. 167-180, 1994.

[10]

{10} Kraaij W. Viewing stemming as recall enhancement. In Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 40-48. ACM, New York. 1996.

Digital Library

[11]

{11} Krovetz R. Viewing Morphology as an Inference Process. In Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 191-202. ACM. New York. 1993.

Digital Library

[12]

{12} Kukushkina O., A. Polikarpov. DicTUM-1: A system for dictionary-text universal manipulations and analysis. In Proc. of XI international Conference "History and Computing". Moscow, August 20-24, 1996.

[13]

{13} Lovins J. Development of a stemming algorithm. Mech. Trans. And Comp. Ling. 11, pp. 22-31. 1968.

[14]

{14} Paice C. Another stemmer. In Proc. of SIGIR Forum, vol. 24(3), pp. 56-61, 1990.

Digital Library

[15]

{15} Popovic M., Willett P. The Effectiveness of Stemming for Natural Language access to Slovene Textual Data. In Journal of The American Society of Information Science. Vol. 43, No 5. pp. 384-390. 1992.

[16]

{16} Porter M. An algorithm for suffix stripping. Program 14, 3. pp. 130-137. 1980.

[17]

{17} Savoy J. Stemming of French words based on grammatical categories. In Journal of the American Society for Information Science. vol. 44(1), pp. 1-9, 1993.

[18]

{18} Schinke R., M. Greengrass, A. Robertson, P. Willett. A stemming algorithm for Latin text databases. In Journal of Documentation. vol. 52, pp.172-187, 1996.

[19]

{19} Silberztein M. Dictionnaires electroniques et analyse automatique de textes: le systeme INTEX. Masson, Paris, 1993.

[20]

{20} Slovoto, http://slovoto.orbitel.bg

[21]

{21} Snowball: http://snowball.tartarus.org

[22]

{22} Xu J., Croft B. Corpus Based Stemming Using Coocurrence of Word Variants. In ACM Transactions on Information Systems, vol. 16, No 1. pp. 61-81. 1998.

Digital Library

Cited By

Kanev GMladenova TValova I(2024)Analyzing Methods for Classification of Electronic Word-of-Mouth: A ReviewProceedings of the Cognitive Models and Artificial Intelligence Conference10.1145/3660853.3660872(85-89)Online publication date: 25-May-2024
https://dl.acm.org/doi/10.1145/3660853.3660872
Dave NMehta MKotecha K(2024)A Systematic Review of Stemmers of Indian and Non-Indian Vernacular LanguagesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/360461223:1(1-51)Online publication date: 15-Jan-2024
https://dl.acm.org/doi/10.1145/3604612
Petrova D(2022)Automatic Sentiment Analysis on Hotel Reviews in Bulgarian—Basic Approaches and ResultsInternational Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing10.1007/978-3-030-92905-3_5(48-56)Online publication date: 5-May-2022
https://doi.org/10.1007/978-3-030-92905-3_5
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Index Terms

Building an inflectional stemmer for Bulgarian
1. Applied computing
  1. Document management and text processing
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Conferences

CompSysTech '03: Proceedings of the 4th international conference conference on Computer systems and technologies: e-Learning

June 2003

732 pages

ISBN:9549641333

DOI:10.1145/973620

Editors:
B. Rachev
Technical University of Varna, Bulgaria
,
A. Smrikarov
University of Rousse, Bulgaria

Copyright © 2003 ACM.

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Published: 19 June 2003

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Cited By

Kanev GMladenova TValova I(2024)Analyzing Methods for Classification of Electronic Word-of-Mouth: A ReviewProceedings of the Cognitive Models and Artificial Intelligence Conference10.1145/3660853.3660872(85-89)Online publication date: 25-May-2024
https://dl.acm.org/doi/10.1145/3660853.3660872
Dave NMehta MKotecha K(2024)A Systematic Review of Stemmers of Indian and Non-Indian Vernacular LanguagesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/360461223:1(1-51)Online publication date: 15-Jan-2024
https://dl.acm.org/doi/10.1145/3604612
Petrova D(2022)Automatic Sentiment Analysis on Hotel Reviews in Bulgarian—Basic Approaches and ResultsInternational Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing10.1007/978-3-030-92905-3_5(48-56)Online publication date: 5-May-2022
https://doi.org/10.1007/978-3-030-92905-3_5
Hristova G(2021)Text Analytics in Bulgarian: An Overview and Future DirectionsCybernetics and Information Technologies10.2478/cait-2021-002721:3(3-23)Online publication date: 7-Dec-2021
https://doi.org/10.2478/cait-2021-0027
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Georgieva-Trifonova T(2021)Modified Pointwise Mutual Information-Based Feature Selection for Text ClassificationProceedings of the Future Technologies Conference (FTC) 2021, Volume 210.1007/978-3-030-89880-9_26(333-353)Online publication date: 4-Nov-2021
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Boytcheva S(2018)Indirect Association Rules Mining in Clinical TextsArtificial Intelligence: Methodology, Systems, and Applications10.1007/978-3-319-99344-7_4(36-47)Online publication date: 29-Aug-2018
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Singh JGupta V(2017)A systematic review of text stemming techniquesArtificial Intelligence Review10.1007/s10462-016-9498-248:2(157-217)Online publication date: 1-Aug-2017
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Singh JGupta V(2016)Text StemmingACM Computing Surveys10.1145/297560849:3(1-46)Online publication date: 16-Sep-2016
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Nikolova IBoytcheva SAngelova GAngelov Z(2016)Combining Structured and Free Textual Data of Diabetic Patients’ Smoking StatusArtificial Intelligence: Methodology, Systems, and Applications10.1007/978-3-319-44748-3_6(57-67)Online publication date: 18-Aug-2016
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