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Detecting inflection patterns in natural language by minimization of morphological model

Authors
Gelbukh, A.Alexandrov, M.Han, S.-Y.
Issue Date
2004
Publisher
Springer Verlag
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.3287, pp 432 - 438
Pages
7
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
3287
Start Page
432
End Page
438
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47142
DOI
10.1007/978-3-540-30463-0_54
ISSN
0302-9743
Abstract
One of the most important steps in text processing and information retrieval is stemming - reducing of words to stems expressing their base meaning, e.g., bake, baked, bakes, baking → bak-. We suggest an unsupervised method of recognition such inflection patterns automatically, with no a priori information on the given language, basing exclusively on a list of words extracted from a large text. For a given word list V we construct two sets of strings: stems S and endings E, such that each word from V is a concatenation of a stem from S and ending from E. To select an optimal model, we minimize the total number of elements in S and E. Though such a simplistic model does not reflect many phenomena of real natural language morphology, it shows surprisingly promising results on different European languages. In addition to practical value, we believe that this can also shed light on the nature of human language. © Springer-Verlag 2004.
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