Comparing words, stems, and roots as index terms in an Arabic Information Retrieval System
Journal of the American Society for Information Science
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Stemming methodologies over individual query words for an Arabic information retrieval system
Journal of the American Society for Information Science
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Term selection for searching printed Arabic
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Improving stemming for Arabic information retrieval: light stemming and co-occurrence analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Part-of-speech tagging using a Variable Memory Markov model
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Scaling to very very large corpora for natural language disambiguation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Building a shallow Arabic Morphological Analyzer in one day
SEMITIC '02 Proceedings of the ACL-02 workshop on Computational approaches to semitic languages
Named entity recognition as a house of cards: classifier stacking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A finite-state morphological grammar of hebrew
Natural Language Engineering
Arabic morphology using only finite-state operations
Semitic '98 Proceedings of the Workshop on Computational Approaches to Semitic Languages
The necessity of syntactic parsing for semantic role labeling
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A probabilistic morphological analyzer for Syriac
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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Words in Semitic languages are formed by combining two morphemes: a root and a pattern. The root consists of consonants only, by default three, and the pattern is a combination of vowels and consonants, with non-consecutive “slots” into which the root consonants are inserted. Identifying the root of a given word is an important task, considered to be an essential part of the morphological analysis of Semitic languages, and information on roots is important for linguistics research as well as for practical applications. We present a machine learning approach, augmented by limited linguistic knowledge, to the problem of identifying the roots of Semitic words. Although programs exist which can extract the root of words in Arabic and Hebrew, they are all dependent on labor-intensive construction of large-scale lexicons which are components of full-scale morphological analyzers. The advantage of our method is an automation of this process, avoiding the bottleneck of having to laboriously list the root and pattern of each lexeme in the language. To the best of our knowledge, this is the first application of machine learning to this problem, and one of the few attempts to directly address non-concatenative morphology using machine learning. More generally, our results shed light on the problem of combining classifiers under (linguistically motivated) constraints.