GWU-HASP: Hybrid Arabic Spelling and Punctuation Corrector

Attia, Mohammed; Al-Badrashiny, Mohamed; Diab, Mona

doi:10.3115/v1/w14-3620

Cited by 17 publications

(11 citation statements)

References 26 publications

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“…The first part detects the spelling mistake in the written text. The second corrects the spelling errors [26,27]. Spelling errors can be identified by using a lexicon of Arabic words.…”

Section: Arabic Automatic Spelling Correction Applicationmentioning

confidence: 99%

Enhancing recurrent neural network-based language models by word tokenization

Noaman

Sarhan

Rashwan

2018

Hum. Cent. Comput. Inf. Sci.

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Statistical language models estimate the probability for a given sequence of words. Given a sentence s with n words such as s = (w 1 , w 2. .. w n), the language model assigns P(s). Statistical language models assess good word sequence estimations based on the sequence probability estimation. Building robust, fast and accurate language models is one of the main factors in the success of building systems such as machine translation systems and automatic speech recognition systems. Statistical language models can be estimated based on various approaches. Classical language models are estimated based on n-gram word sequences such as P(s) = P(w 1 , w 2. .. w n) = n i=1 P(w i |w i−1) and can be approximated based on the Markov concept for shorter contexts (for example, bigram if n = 2 or trigram if n = 3 and so on). Recent researchers have applied neural networks different architectures to build and estimate language models. The classical feed-forward neural network-based language models have been continuously reporting good results among the traditional n-gram language modeling techniques [1]. It

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“…The first part detects the spelling mistake in the written text. The second corrects the spelling errors [26,27]. Spelling errors can be identified by using a lexicon of Arabic words.…”

Section: Arabic Automatic Spelling Correction Applicationmentioning

confidence: 99%

Enhancing recurrent neural network-based language models by word tokenization

Noaman

Sarhan

Rashwan

2018

Hum. Cent. Comput. Inf. Sci.

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“…Some spelling errors are due to partial or total ignorance of words heard by students, i.e. geograthathy instead of geography (Attia et al, 2014).…”

Section: Studies Of Spelling Errors In a Non-arabic Contextmentioning

confidence: 99%

A Review of Spelling Errors in Arabic and Non-Arabic Contexts

Altamimi¹,

Rashid²,

Elhassan³

2018

ELT

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The purpose of this review paper is to identify the core spelling errors in Arabic and Non Arabic Contexts. The most common difficulty that Arab learners may face during their English writing is correct spelling, for many different reasons such as the interference between English and Arabic language and the irregularity of the English language system. Several studies have been undertaken to evaluate writing mistakes and spelling errors in English, and most of them have classified spelling errors into three different categories: morphemic errors, where the errors occur in the morphemes parts (prefixes and suffixes); Intra-Morphemic errors, where errors occur in the word roots themselves such as deleting the final (silent) e vowel in the word write, and splits types, where the learners leave a space inside the word for example, write my self as two words instead of myself, one word. Apart from the three categories mentioned above, other studies claim that there are eight different types of error related to the abilities of the students and the nature of the error, and these include inversion, omission, substitution, segmentation, insertion, pronunciation, miscellaneous, and unclassified errors. In this review paper, we have found interlingual and intralingual –related errors where interlingual errors are mainly caused by the interference of the primary or mother language, while, intralingual errors are due to the system and instruction of the target language. Finally, suggestions are given based on previous research about how to review the spelling errors in Arabic and Non Arabic contexts to identify the error and also overcome the problem through alternatives that can be implemented to create a positive impact and can be furthermore used for all types of positive learning.

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“…Error detection: Techniques used in the literature for detecting Arabic spelling errors are essentially based on two approaches: the language rules (AlShenaifi et al, 2015;Shaalan et al, 2010;Hassan et al, 2014) or a dictionary (Attia et al, 2014;Zerrouki et al, 2014;. For the first technique, detecting whether a word is misspelled or not depends on morphological analyzers.…”

Section: Auto-correction Of Hamozamentioning

confidence: 99%

Proceedings of the Third Arabic Natural Language Processing Workshop

Habash¹,

Diab²,

Darwish³

et al. 2017

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A number of Arabic NLP (or Arabic NLP-related) workshops and conferences have taken place in the last few years, both in the Arab World and in association with international conferences. The Arabic NLP workshop at EACL 2017 follows in the footsteps of these previous efforts to provide a forum for researchers to share and discuss their ongoing work. This particular workshop is the third in a series, following the First Arabic NLP workshop held at EMNLP 2014 in Doha, Qatar; and the Second Arabic NLP workshop held at ACL 2015 in Beijing, China.We received 47 submissions and selected 22 (47% acceptance rate) for presentation in the workshop. All papers were reviewed by three reviewers on average. The number of submissions is over twice that of the previous workshop in Beijing, which also had a higher acceptance rate (65%). Ten papers will be presented orally and 12 as part of a poster session. The presentation mode is independent of the ranking of the papers. The papers cover a diverse set of topics from Maltese and Arabic dialect processing to models of semantic similarity and credibility analysis, advances in Arabic treebanking, and error annotation for dyslexic texts.The quantity and quality of the contributions to the workshop are strong indicators that there is a continued need for this kind of dedicated Arabic NLP workshop.We would like to acknowledge all the hard work of the submitting authors and thank the reviewers for the valuable feedback they provided. We hope these proceedings will serve as a valuable reference for researchers and practitioners in the field of Arabic NLP and NLP in general.Nizar Habash, General Chair, on behalf of the organizers of the workshop. AbstractThis paper presents a language identification system designed to detect the language of each word, in its context, in a multilingual documents as generated in social media by bilingual/multilingual communities, in our case speakers of Algerian Arabic. We frame the task as a sequence tagging problem and use supervised machine learning with standard methods like HMM and Ngram classification tagging. We also experiment with a lexicon-based method. Combining all the methods in a fall-back mechanism and introducing some linguistic rules, to deal with unseen tokens and ambiguous words, gives an overall accuracy of 93.14%. Finally, we introduced rules for language identification from sequences of recognised words.

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GWU-HASP: Hybrid Arabic Spelling and Punctuation Corrector

Cited by 17 publications

References 26 publications

Enhancing recurrent neural network-based language models by word tokenization

Enhancing recurrent neural network-based language models by word tokenization

A Review of Spelling Errors in Arabic and Non-Arabic Contexts

Proceedings of the Third Arabic Natural Language Processing Workshop

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