Instance-Based Learning Algorithms
Machine Learning
Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
Information Retrieval
Arabic morphology generation using a concatenative strategy
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Nonconcatenative finite-state morphology
EACL '87 Proceedings of the third conference on European chapter of the Association for Computational Linguistics
Consonant spreading in Arabic stems
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Multi-tape two-level morphology: a case study in semitic non-linear morphology
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Memory-based morphological analysis
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Automatic tagging of Arabic text: from raw text to base phrase chunks
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
A rule-based approach to unknown word recognition in Arabic
SIGMORPHON '12 Proceedings of the Twelfth Meeting of the Special Interest Group on Computational Morphology and Phonology
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We explore the application of memory-based learning to morphological analysis and part-of-speech tagging of written Arabic, based on data from the Arabic Treebank. Morphological analysis -- the construction of all possible analyses of isolated unvoweled wordforms -- is performed as a letter-by-letter operation prediction task, where the operation encodes segmentation, part-of-speech, character changes, and vocalization. Part-of-speech tagging is carried out by a bi-modular tagger that has a subtagger for known words and one for unknown words. We report on the performance of the morphological analyzer and part-of-speech tagger. We observe that the tagger, which has an accuracy of 91.9% on new data, can be used to select the appropriate morphological analysis of words in context at a precision of 64.0 and a recall of 89.7.