Abstract
Unsupervised morphemic analysis may be divided into two phases: 1) Establishment of an initial morpheme set, and 2) optimization of this generally imperfect first approximization. This paper focuses on the first phase, that is the establishment of an initial morphemic analysis, whereby methodological questions regarding ‘unsupervision’ will be touched on. The basic algorithm for segmentation employed goes back to Harris (1955). Proposals for the antecedent transformation of graphemic representations into (partial) phonemic ones are discussed as well as the postprocessing step of reapplying the initially gained morphemic candidates. Instead of directly using numerical (count) measures, a proposal is put forward which exploits numerical interpretations of a universal morphological assumption on morphemic order for the evaluation of the computationally gained segmantations and their quantitative properties.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
BARONI, M. (2003): Distribution-driven morpheme discovery: A computational experimental study. Yearbook of Morphology 2003, 213–248.
BENDEN, C. (2005): Automated Detection of Morphemes Using Distributional Measurements. In: C. Weihs and W. Gaul (Eds.) Classification — The Ubiquitous Challenge. Proceedings of the 28th Annual Conference of the Gesellschaft für Klassifikation, Dortmund University. Springer, Berlin, 490–497.
BERNSTEIN, J. and PISONI, D. (1980): Unlimited text-to-speech system: Description and evaluation of a microprocessor based device. Proceedings of the 5th International Conference on Acoustics, Speech, and Signal Processing. Denver, 576–579
DÉJEAN, H. (1998). Morphemes as Necessary Concepts for Structures Discovery from Untagged Corpora. Workshop on Paradigms and Grounding in Natural Language Learning. Adelaide, 295–299.
GOLDSMITH, J. (2001): Unsupervised Learing of the Morphology of a Natural Language. Computational Linguistics, 27.2, 153–198.
HAFER, M. and WEISS, S. (1974): Word Segmentation by Letter Successor Varieties. Information Storage and Retrieval, 10, 371–385.
HARRIS, Z. (1955): From Phoneme to Morpheme. Language, 31, 190–222.
MARTINET, A. (1960): Eléments de linguistique générale. Paris, Librairie Armand Colin.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer Berlin · Heidelberg
About this paper
Cite this paper
Benden, C. (2006). Bootstrapping an Unsupervised Morphemic Analysis. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_38
Download citation
DOI: https://doi.org/10.1007/3-540-31314-1_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31313-7
Online ISBN: 978-3-540-31314-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)