Discovering lexical information by tagging Arabic newspaper text

Abuleil, Saleem; Evens, Martha

doi:10.3115/1621753.1621755

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…Hmeidi, Kanaan, and Evens () describe how to build an automatic indexing system for Arabic text with comparable accuracy to human indexing systems. In their study, Abuleil and Evens () show how to automatically build a large Arabic integrated and comprehensive lexicon. They developed a part of speech (POS) tagger for Arabic text to extract features of the Arabic words encountered.…”

Section: Related Workmentioning

confidence: 99%

Automated arabic text classification with P‐Stemmer, machine learning, and a tailored news article taxonomy

Kanan

Fox

2015

Asso for Info Science & Tech

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Arabic news articles in electronic collections are difficult to work with. Browsing by category is rarely supported. While helpful machine learning methods have been applied successfully to similar situations for English news articles, limited research has been completed to yield suitable solutions for Arabic news. In connection with a QNRF funded project to build digital library community and infrastructure in Qatar, we developed software for browsing a collection of about 237K Arabic news articles, which should be applicable to other Arabic news collections as well. We designed a simple taxonomy for Arabic news stories that is suitable for the needs in Qatar and other nations, is compatible with the subject codes of the International Press Telecommunications Council, and was enhanced with the aid of a librarian expert as well as five Arabic-speaking volunteers. We developed tailored stemming (i.e., a new Arabic light stemmer) and automatic classification methods (the best being binary SVM classifiers) to work with the taxonomy. Using evaluation techniques commonly used in the information retrieval community, including 10-fold cross-validation and the Wilcoxon signed-rank test, we showed that our approach to stemming and classification is superior to state-of-the-art techniques.

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Section: Related Workmentioning

confidence: 99%

Automated arabic text classification with P‐Stemmer, machine learning, and a tailored news article taxonomy

Kanan

Fox

2015

Asso for Info Science & Tech

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“…‫ﺷﻬﺮ‬ Month ‫ﻣﺪﻱﻨﺔ‬ city ‫ﺷﻤﺎل‬ north A proper noun is the name of a specific person, place, organization, thing, idea, event, date, time, or other entity. Some of them are solid (inert) nouns some of them are derived [Abuleil and Evens 1998].…”

Section: ‫ﻣﺤﺎﺿﺮ‬ Professormentioning

confidence: 99%

Acquisition system for Arabic noun morphology

Abuleil

Alsamara

Evens

2002

Proceedings of the ACL-02 Workshop on Computational Approaches to Semitic Languages -

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Many papers have discussed different aspects of Arabic verb morphology. Some of them used patterns; others used patterns and affixes. But very few have discussed Arabic noun morphology particularly for nouns that are not derived from verbs. In this paper we describe a learning system that can analyze Arabic nouns to produce their morphological information and their paradigms with respect to both gender and number using a rule base that uses suffix analysis as well as pattern analysis. The system utilizes user-feedback to classify the noun and identify the group that it belongs to.

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“…Several studies suggested that indexing Arabic text using roots significantly increases retrieval effectiveness over the use of words (Abuleil and Evens, 1998;Hammo et al, 2002;Habash and Rambow, 2005;Khoja, 1999;Al-Kharashi and Evens, 1994).…”

Section: Stemming-root Extractionmentioning

confidence: 99%

“…It is critical for parsing, text generation, text summarization and for question answering systems (Abuleil and Evens, 1998). Furthermore, Information Retrieval is one of the first areas of natural language processing in which statistics were successfully applied (Hiemstra and De vries, 2000).…”

Section: Introductionmentioning

confidence: 99%

QArabPro: A Rule Based Question Answering System for Reading Comprehension Tests in Arabic

Akour

Abufardeh²,

Magel

et al. 2011

American Journal of Applied Sciences

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Problem statement: Extensive research efforts in the area of Natural Language Processing (NLP) were focused on developing reading comprehension Question Answering systems (QA) for Latin based languages such as, English, French and German. Approach: However, little effort was directed towards the development of such systems for bidirectional languages such as Arabic, Urdu and Farsi. In general, QA systems are more sophisticated and more complex than Search Engines (SE) because they seek a specific and somewhat exact answer to the query. Results: Existing Arabic QA system including the most recent described excluded one or both types of questions (How and Why) from their work because of the difficulty of handling these questions. In this study, we present a new approach and a new questionanswering system (QArabPro) for reading comprehension texts in Arabic. The overall accuracy of our system is 84%. Conclusion/Recommendations: These results are promising compared to existing systems. Our system handles all types of questions including (How and why).

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Discovering lexical information by tagging Arabic newspaper text

Cited by 14 publications

References 8 publications

Automated arabic text classification with P‐Stemmer, machine learning, and a tailored news article taxonomy

Automated arabic text classification with P‐Stemmer, machine learning, and a tailored news article taxonomy

Acquisition system for Arabic noun morphology

QArabPro: A Rule Based Question Answering System for Reading Comprehension Tests in Arabic

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