Search

Your search keyword '"Lakhouaja, Abdelhak"' showing total 10 results
Start Over Author "Lakhouaja, Abdelhak" Search Limiters Full Text
10 results on '"Lakhouaja, Abdelhak"'

9. MulTed: a multilingual aligned and tagged parallel corpus.

Author

Zeroual, Imad and Lakhouaja, Abdelhak

Subjects
MACHINE translating, NATURAL language processing, CORPORA, PARTS of speech
Abstract

Recently, more data-driven approaches are demanding multilingual parallel resources primarily in the crosslanguage studies. To meet these demands, building multilingual parallel corpora are becoming the focus of many Natural Language Processing (NLP) scientific groups. Unlike monolingual corpora, the number of available multilingual parallel corpora is limited. In this paper, the MulTed, a corpus of subtitles extracted from TEDx talks is introduced. It is multilingual, Part of Speech (PoS) tagged, and bilingually sentence-aligned with English as a pivot language. This corpus is designed for many NLP applications, where the sentencealignment, the PoS tagging, and the size of corpora are influential such as statistical machine translation, language recognition, and bilingual dictionary generation. Currently, the corpus has subtitles that cover 1100 talks available in over 100 languages. The subtitles are classified based on a variety of topics such as Business, Education, and Sport. Regarding the PoS tagging, the Treetagger, a language-independent PoS tagger, is used; then, to make the PoS tagging maximally useful, a mapping process to a universal common tagset is performed. Finally, we believe that making the MulTed corpus available for a public use can be a significant contribution to the literature of NLP and corpus linguistics, especially for under-resourced languages. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

10. Hybrid approaches for automatic vowelization of Arabic texts

Author

Lakhouaja Abdelhak, Mazroui Azzeddine, Chennoufi Amine, and Mohamed Bebah

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, biology, Process (engineering), business.industry, Computer science, 68T50, Context (language use), Morpho, Viterbi algorithm, computer.software_genre, Lexical database, biology.organism_classification, symbols.namesake, Path (graph theory), symbols, Artificial intelligence, business, Hidden Markov model, Computation and Language (cs.CL), computer, Natural language processing, Word (computer architecture)
Abstract

Hybrid approaches for automatic vowelization of Arabic texts are presented in this article. The process is made up of two modules. In the first one, a morphological analysis of the text words is performed using the open source morphological Analyzer AlKhalil Morpho Sys. Outputs for each word analyzed out of context, are its different possible vowelizations. The integration of this Analyzer in our vowelization system required the addition of a lexical database containing the most frequent words in Arabic language. Using a statistical approach based on two hidden Markov models (HMM), the second module aims to eliminate the ambiguities. Indeed, for the first HMM, the unvowelized Arabic words are the observed states and the vowelized words are the hidden states. The observed states of the second HMM are identical to those of the first, but the hidden states are the lists of possible diacritics of the word without its Arabic letters. Our system uses Viterbi algorithm to select the optimal path among the solutions proposed by Al Khalil Morpho Sys. Our approach opens an important way to improve the performance of automatic vowelization of Arabic texts for other uses in automatic natural language processing., 19 pages

Published
2014

Catalog

Books, media, physical & digital resources
See catalog results