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EMNLP
2010

A New Approach to Lexical Disambiguation of Arabic Text

13 years 8 months ago
A New Approach to Lexical Disambiguation of Arabic Text
We describe a model for the lexical analysis of Arabic text, using the lists of alternatives supplied by a broad-coverage morphological analyzer, SAMA, which include stable lemma IDs that correspond to combinations of broad word sense categories and POS tags. We break down each of the hundreds of thousands of possible lexical labels into its constituent elements, including lemma ID and part-of-speech. Features are computed for each lexical token based on its local and document-level context and used in a novel, simple, and highly efficient two-stage supervised machine learning algorithm that overcomes the extreme sparsity of label distribution in the training data. The resulting system achieves accuracy of 90.6% for its first choice, and 96.2% for its top two choices, in selecting among the alternatives provided by the SAMA lexical analyzer. We have successfully used this system in applications such as an online reading helper for intermediate learners of the Arabic language, and a to...
Rushin Shah, Paramveer S. Dhillon, Mark Liberman,
Added 11 Feb 2011
Updated 11 Feb 2011
Type Journal
Year 2010
Where EMNLP
Authors Rushin Shah, Paramveer S. Dhillon, Mark Liberman, Dean P. Foster, Mohamed Maamouri, Lyle H. Ungar
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