An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
IEEE Intelligent Systems
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
An HMM approach to vowel restoration in Arabic and Hebrew
SEMITIC '02 Proceedings of the ACL-02 workshop on Computational approaches to semitic languages
Maximum entropy based restoration of Arabic diacritics
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for 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
Arabic diacritization through full morphological tagging
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Part of speech tagging for arabic
Natural Language Engineering
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Modern standard Arabic is usually written without diacritics. This makes it difficult for performing Arabic text processing. Diacritization helps clarify the meaning of words and disambiguate any vague spellings or pronunciations, as some Arabic words are spelled the same but differ in meaning. In this paper, we address the issue of adding diacritics to undiacritized Arabic text using a hybrid approach. The approach requires an Arabic lexicon and large corpus of fully diacritized text for training purposes in order to detect diacritics. Case-Ending is treated as a separate post processing task using syntactic information. The hybrid approach relies on lexicon retrieval, bigram, and SVM-statistical prioritized techniques. We present results of an evaluation of the proposed diacritization approach and discuss various modifications for improving the performance of this approach.