Evaluation of the ambiguity caused by the absence of diacritical marks in Arabic texts: Statistical study

Boudchiche, Mohamed; Mazroui, Azzeddine

doi:10.1109/icta.2015.7426904

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…Habash [5] argues that on average a non-diacritized word can have 12 different morphological analyses. Similarly, in the statistical study conducted in [4] on a corpus of more than 82 million non-diacritized words, the authors showed that a word analysed out of context has on average 2.63 roots, 5.11 lemmas, 4.79 stems and 3.86 POS tags.…”

Section: Introductionmentioning

confidence: 88%

“…Ambiguity is very present in the Arabic language because of its agglutinating and derivational characteristics [3]. Moreover, the absence of diacritical marks in the vast majority of Arabic texts greatly amplifies ambiguity [4]. For example, the non-diacritized word " ‫ﻓ‬ ‫ﺮ‬ ‫ﻣ‬ ‫ﺖ‬ " /frmt/ 1 may be the verb " ‫ﻓ‬ َ ‫ﺮ‬ َ ‫ﻣ‬ َ ‫ﺖ‬ ْ " /faramato/ that has two meanings depending on its context: (and she threw) whose root is " ‫ﺭ‬ ‫ﻡ‬ ‫ﻱ‬ " /r m y/, or (she cut) whose root is " ‫ﻑ‬ ‫ﺭ‬ ‫ﻡ‬ " /frm/.…”

Section: Introductionmentioning

confidence: 99%

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Spline functions for Arabic morphological disambiguation

Boudchiche

Mazroui

2020

ACI

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We have developed in this paper a morphological disambiguation hybrid system for the Arabic language that identifies the stem, lemma and root of a given sentence words. Following an out-of-context analysis performed by the morphological analyser Alkhalil Morpho Sys, the system first identifies all the potential tags of each word of the sentence. Then, a disambiguation phase is carried out to choose for each word the right solution among those obtained during the first phase. This problem has been solved by equating the disambiguation issue with a surface optimization problem of spline functions. Tests have shown the interest of this approach and the superiority of its performances compared to those of the state of the art.

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Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Spline functions for Arabic morphological disambiguation

Boudchiche

Mazroui

2020

ACI

Self Cite

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“…Apart of this, the absence of diacritical marks is a foundation of intricacy for processing Arabic automatic systems language that the sentence meaning cannot be determined easily [15]. Moreover, the shortage of Arabic diacritical marks in the sentences reflects the major reason of the muddle encounter throughout analysis stage [16]. Over and above, the study of [17] demonstrated that the diacritization automatic content increments manual quality labelling of the corpus.…”

Section: Introductionmentioning

confidence: 99%

Framework of diacritic segmentation for Arabic handwritten document

Shiekh

Azmi

Aziz

et al. 2021

IJEECS

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<span lang="EN-US">In <span>recent Arabic standard language and Arabic dialectal texts, diacritics and short vowels are absent. There are some exceptions have been made for the Arabic beginner learner scripts, religious texts and as well as a significant political text. In addition, the text without diacritics is considered ambiguous due to numerous words with different diacritic marks seem identical. However, this paper we present a framework for segmenting diacritics from Arabic handwritten document by using region-based segmentation technique. Since Arabic handwritten and Mushaf Al-Quran contain many diacritical marks. Hence, the diacritics must be properly extracted from Arabic handwritten document to avoid losing some good features. Furthermore, the proposed framework is devised specifically to segment diacritics from Arabic handwritten image, thus there will be no feature extraction, feature selection, and classification processes included. Besides, we will present the methodology that is used to fulfil the objectives of this paper. The pre-processing phases will be explained and more specifically segmentation phase for segmenting diacritics which is the phase we concentrate more in this article. Lastly, we will identify the proposed technique region-based segmentation to facilitate our development throughout the experimental process.</span></span>

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“…According to [2], in over 77% of cases, a non-vocalized word can have several possible diacritizations and consequently different possible meanings. Table 1 gives an example of this aspect and lists some of the possible diacritization forms of the string ‫"صدق"‬ and the inferred meaning.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Gated Recurrent Unit in Arabic Diacritization

Moumen¹,

Chiheb²,

Faizi³

et al. 2018

ijacsa

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Recurrent neural networks are powerful tools giving excellent results in various tasks, including Natural Language Processing tasks. In this paper, we use Gated Recurrent Unit, a recurrent neural network implementing a simple gating mechanism in order to improve the diacritization process of Arabic. Evaluation of Gated Recurrent Unit for diacritization is performed in comparison with the state-of-the art results obtained with Long-Short term memory a powerful RNN architecture giving the best-known results in diacritization. Evaluation covers two performance aspects, Error rate and training runtime.

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Evaluation of the ambiguity caused by the absence of diacritical marks in Arabic texts: Statistical study

Cited by 12 publications

References 8 publications

Spline functions for Arabic morphological disambiguation

Spline functions for Arabic morphological disambiguation

Framework of diacritic segmentation for Arabic handwritten document

Evaluation of Gated Recurrent Unit in Arabic Diacritization

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