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@article{558001,
author = {Šmerk, Pavel},
article_location = {Berlin},
article_number = {1},
keywords = {morphological disambiguation;tagging;morphological tagging;unsupervised learning},
language = {eng},
issn = {0302-9743},
journal = {Lecture Notes in Computer Science},
title = {Unsupervised Learning of Rules for Morphological Disambiguation},
volume = {3206},
year = {2004}
}
TY - JOUR
ID - 558001
AU - Šmerk, Pavel
PY - 2004
TI - Unsupervised Learning of Rules for Morphological Disambiguation
JF - Lecture Notes in Computer Science
VL - 3206
IS - 1
SP - 211-216
EP - 211-216
PB - Springer Verlag
SN - 03029743
KW - morphological disambiguation;tagging;morphological tagging;unsupervised learning
N2 - State-of-the-art rule-based tools for morphological disambiguation use either manually crafted rules or rules learnt from manually annotated data. This paper presents a new method of learning rules for morphological disambiguation using only unannotated data. The inductive logic programming and active learning are employed. The induced rules display very promising acurracy. Also the probable limitations of the proposed method are discussed.
ER -
ŠMERK, Pavel. Unsupervised Learning of Rules for Morphological Disambiguation. \textit{Lecture Notes in Computer Science}. Berlin: Springer Verlag, roč.~3206, č.~1, s.~211-216. ISSN~0302-9743. 2004.
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