Arabic morphological tagging, diacritization, and lemmatization using lexeme models and feature ranking
Published 1 January 2008Open access
Ryan M. Roth, Owen Rambow, Nizar Habash, Mona Diab, Cynthia Rudin
Citations152
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This work investigates the tasks of general morphological tagging, diacritization, and lemmatization for Arabic and shows that for all tasks, both modeling the lexeme explicitly and retuning the weights of individual classifiers for the specific task, improve the performance.
Abstract
We investigate the tasks of general morphological tagging, diacritization, and lemmatization for Arabic. We show that for all tasks we consider, both modeling the lexeme explicitly, and retuning the weights of individual classifiers for the specific task, improve the performance.
Keywords
Computer Science
The Computer JournalA Simplex Method for Function Minimization
28,856 Citations1965J. A. Nelder, R. Mead
A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop
443 Citations2005Nizar Habash, Owen Rambow
An approach to using a morphological analyzer for tokenizing and morphologically tagging Arabic words in one process using classifiers for individual morphological features, as well as ways of using these classifiers to choose among entries from the output of the analyzer.
Maximum entropy based restoration of Arabic diacritics
140 Citations2006Imed Zitouni, Jeffrey Sorensen +1 more
A maximum entropy approach for restoring diacritics in a document that can easily integrate and make effective use of diverse types of information and integrates a wide array of lexical, segment-based and part-of-speech tag features.
Arabic diacritization through full morphological tagging
139 Citations2007Nizar Habash, Owen Rambow
A diacritization system for written Arabic which is based on a lexical resource which combines a tagger and a lexeme language model which improves on the best results reported in the literature.
Morphological tagging: data vs. dictionaries
108 Citations2000Jan Hajič
It is argued strongly that the use of an independent morphological dictionary is the preferred choice to more annotated data under such circumstances as data sparseness issue for inflectionally rich languages.