Publication details

Unsupervised Learning of Rules for Morphological Disambiguation

Authors

ŠMERK Pavel

Year of publication 2004
Type Article in Periodical
Magazine / Source Lecture Notes in Computer Science
MU Faculty or unit

Faculty of Informatics

Citation
Field Informatics
Keywords morphological disambiguation;tagging;morphological tagging;unsupervised learning
Description 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.

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