Generating Arabic text in multilingual speech-to-speech machine translation framework

Monem, Azza Abdel; Shaalan, Khaled; Rafea, Ahmed; Baraka, H.

doi:10.1007/s10590-009-9054-9

Cited by 20 publications

(7 citation statements)

References 27 publications

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“…The new grammar also must describe important aspects such as anaphoric relations, the subjectless sentences, and discourse analysis. In spite of these challenges, significant work has been done in ANLP in applications such as entity extraction [Shaalan and Raza 2009], machine translation [Farghaly and Senellart 2003;Shaalan et al 2004;Fraser and Wong 2009;Sawaf 2009;Abdel Monem et al 2009], and sentiment analysis [Almas and Ahmed 2007].…”

Section: Resultsmentioning

confidence: 99%

Arabic Natural Language Processing

Farghaly

Shaalan

2009

ACM Transactions on Asian Language Information Processing

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395

218

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The Arabic language presents researchers and developers of natural language processing (NLP) applications for Arabic text and speech with serious challenges. The purpose of this article is to describe some of these challenges and to present some solutions that would guide current and future practitioners in the field of Arabic natural language processing (ANLP). We begin with general features of the Arabic language in Sections 1, 2, and 3 and then we move to more specific properties of the language in the rest of the article. In Section 1 of this article we highlight the significance of the Arabic language today and describe its general properties. Section 2 presents the feature of Arabic Diglossia showing how the sociolinguistic aspects of the Arabic language differ from other languages. The stability of Arabic Diglossia and its implications for ANLP applications are discussed and ways to deal with this problematic property are proposed. Section 3 deals with the properties of the Arabic script and the explosion of ambiguity that results from the absence of short vowel representations and overt case markers in contemporary Arabic texts. We present in Section 4 specific features of the Arabic language such as the nonconcatenative property of Arabic morphology, Arabic as an agglutinative language, Arabic as a pro-drop language, and the challenge these properties pose to ANLP. We also present solutions that have already been adopted by some pioneering researchers in the field. In Section 5 we point out to the lack of formal and explicit grammars of Modern Standard Arabic which impedes the progress of more advanced ANLP systems. In Section 6 we draw our conclusion.

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Section: Resultsmentioning

confidence: 99%

Arabic Natural Language Processing

Farghaly

Shaalan

2009

ACM Transactions on Asian Language Information Processing

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395

218

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“…The informal style is the used language between families. Classical Arabic is the expression of the Quran (Abdel Monem et al, 2008). So students who analyze the Quran or Hadith should follow classical Arabic.…”

Section: Arabic and Its Variationsmentioning

confidence: 99%

A Comparison between Conditional Random Field and Structured Support Vector Machine for Arabic Named Entity Recognition

Muhammad¹,

Rohaim²,

Hamouda³

et al. 2020

Journal of Computer Science

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The Named Entity Recognition (NER) is an integrated task in many NLP applications such as machine translation, Information extraction and question answering. Arabic is one of the authorised spoken languages in the united nation. Currently, there is much Arabic information on the internet, so, nowadays the need for tools which process this information becomes significant. In this study, we have examined the impact of the conditional random field and the structured support vector machine in the task of Arabic NER. The structured support vector machine is the first time to be applied in the Arabic name entity recognition. Our proposed system has three stages: Preprocessing, extracting features and building model. We have used simple features like the bag of words in the [-1,1] window, the bag of part of speech tag in the [-1,1] window to enable our system to detect the multi-words entities. Also, we have tried to enhance the Stanford part of speech tagger to enhance the tagger output tags, which enabled our system to differentiate between the name entities from the nonentities. In addition, we have employed the binary features of: Is a person, is a prename, is a pre-location, is a location and is an organization. Our system has been trained and tested on part of ANER Crop. The results have proved that the conditional random field-based Arabic NER system outperforms the structured support vector machine-based Arabic NER using the same features set.

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“…Arabic is a Semitic language spoken by around 350 million people, but it is understood among Muslims around the world that is four times larger, (Abdel-Monem et al, 2009). In Arabic, there is classical Arabic, Modern Standard Arabic (MSA) and spoken Arabic.…”

Section: Introductionmentioning

confidence: 99%

Modern Standard Arabic and Rural Palestinian Dialect: Patterns of the Active Participle

Mohaidat¹

2017

IES

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This paper was mainly concerned with analyzing the processes of active participle formation in Modern Standard Arabic (MSA). It also aimed to examine the Rural Palestinian Dialect (RPD) in order to reveal the derivation of the active participle in this dialect and to describe any patterns that might vary from MSA. The study was based on the traditional notions of root and pattern which characterize Arabic morphology. The data for this study were collected from various sources. These sources are not researchers but they are people originated from Palestine. Then the data were analyzed in terms of morphology.

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Generating Arabic text in multilingual speech-to-speech machine translation framework

Cited by 20 publications

References 27 publications

Arabic Natural Language Processing

Arabic Natural Language Processing

A Comparison between Conditional Random Field and Structured Support Vector Machine for Arabic Named Entity Recognition

Modern Standard Arabic and Rural Palestinian Dialect: Patterns of the Active Participle

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