Grammar-Lexis Relations in the Computational Morphology of Arabic

Dichy, Joseph; Farghaly, Ali

doi:10.1007/978-1-4020-6046-5_7

Cited by 5 publications

(5 citation statements)

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“…BAMA 1.0 is available for public use, while BAMA 2.0 and Standard Arabic Morphological Analyzer (SAMA) 3.0 [29] are available through the Linguistic Data Consortium (LDC). [33] present the significance of syntactic features in Arabic computational morphology in detail. Systems based on this method produce a higher level of morphological analyzers, called morphosyntactic analyzers.…”

Section: A Linguistic Lexicon-based Approachmentioning

confidence: 98%

“…There have been many systems attempting to model Arabic morphology based on McCarthy's theorem. Most of these systems adopted finite-state language modelling tools [33].…”

Section: A Linguistic Lexicon-based Approachmentioning

confidence: 99%

“…a) Stems based on root-pattern morphology: Briefly, this morphology can be described as follows: each existing lexical entry is checked against candidate entries integrating root and pattern (to generate a stem), in addition to prefix or suffix combinations. Therefore, if the lexicon in this morphology contains, for example, X root and Y pattern, then the XY root-pattern virtual links represent all possible stems, which must be severely restricted to give a reasonable number of meaningful words [33].…”

Section: A Linguistic Lexicon-based Approachmentioning

confidence: 99%

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Arabic Morphological Analysis Techniques

Alothman¹,

Al-Salman²

2020

IJACSA

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Recently, activity surrounding Arabic natural language processing has increased significantly. Morphological analysis is the basis of most tasks related to Arabic natural language processing. There are many scientific studies on Arabic morphological analysis, yet most of them lack an accurate classification of Arabic morphology and fail to cover both recent and traditional techniques. This paper aims to survey Arabic morphological analysis techniques from 2005 to 2019 and to organize them into a reasonable and expandable classification system. To facilitate and support new research, this paper compares the currently available Arabic morphological analyzers, reaches certain conclusions, and proposes some promising directions for future research in Arabic morphological analysis.

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Section: A Linguistic Lexicon-based Approachmentioning

confidence: 98%

“…There have been many systems attempting to model Arabic morphology based on McCarthy's theorem. Most of these systems adopted finite-state language modelling tools [33].…”

Section: A Linguistic Lexicon-based Approachmentioning

confidence: 99%

Section: A Linguistic Lexicon-based Approachmentioning

confidence: 99%

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Arabic Morphological Analysis Techniques

Alothman¹,

Al-Salman²

2020

IJACSA

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“…Large lexical gaps are eliminated, and each entry can be connected to pertinent and precise morphological, syntactic, and semantic characteristics like gender, number, person, definiteness, Part-of-Speech (POS), etc. (Dichy & Farghaly, 2007).…”

Section: Research Techniquesmentioning

confidence: 99%

A Computational Analysis of Arabic Noun Morphology

Salih

Devi²,

Ali³

et al. 2023

IJLLT

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This project looks at Arabic word generation from a computational angle. It focuses on the computational production and analysis of morphological Arabic nouns. The work begins with a stem-based descriptive analysis of Arabic noun morphology that fulfills both the computational formalization and the linguistic description. There includes a thorough discussion of both inflectional and derivational systems. The spelling of Arabic nouns is also covered, as well as morphotactics and morphophonemics. The work then offers a computer implementation of Arabic nouns built on a rule-based computational morphological methodology. The overall system is constructed using the NooJ toolkit, which supports both pushdown automata and finite-state automata (FSA) (PDA). Three elements make up the morphological generation and analysis system: a lexicon, morphotactics, and rules. The lexicon component catalogs lexical elements (indivisible words and affixes), the morphotactics component specifies ordering restrictions for morphemes, and the rules component converts lexical representations into surface representations and vice versa. Other rules, such as orthographic, morphophonemic, and morphological rules, are also stored as two-level rules. The core editable lexicon of lemmas used as input by the system is drawn from three sources: the Buckwalter Arabic morphological analyzer lexicon, the Arramooz machine-readable dictionary, and the Alghani Azzahir dictionary. A complete annotated vocabulary of inflected noun forms (combined into a single type of finite-state transducers (FSTs)) is the system's output. The lexicon that was developed is then put to use in morphological analysis. The study then offers the system's evaluation. Accuracy, precision, and recall are three widely used metrics to assess the system's performance. Two empirical experiments will be conducted as part of the evaluation task. The system analyzing Arabic words that have been discredited morphologically is evaluated in the first experiment. Accuracy, precision, and recall for the system when employing discredited Arabic words are (90.4%), (98.3%), and (88.9%), respectively. The technique is tested in a second experiment using undiacritical words. The achieved outcomes of this experiment were (94.7%) accuracy, (96.7%) precision, and (91.6% ) recall, respectively. Additionally, the measurement average for the two tests has been determined. The average performance values are respectively (92.55%), (97.5%), and (90.25) percent in terms of recall, precision, and accuracy. Overall, the results are encouraging and demonstrate the system's propensity for dealing with both diacritically and undiacritically written Arabic texts. This system can analyze Arabic text corpora in-depth and tag nouns according to their morphological characteristics. It breaks the word under analysis into three pieces (the stem, proclitics/prefixes, and suffixes/enclitics) and assigns each one a specific morphological feature tag or possibly many tags if the portion in question has numerous clitics or affixes. Many applications of natural language processing, including parsing, lemmatization, stemming, part-of-speech (POS) tagging, corpus annotations, word sense disambiguation, machine translation, information retrieval, text generation, spelling checkers, etc., depend on computational morphology. It is made up of morphological generation and analysis paradigms. According to a set of features, morphological generation attempts to construct every feasible derived and inflected form of a given lemma. On the other hand, morphological analysis is the process of dissecting a word into its component morphemes and giving each morpheme linguistic tags or qualities.

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“…These challenges have been addressed extensively in Arabic NLP research which has focused on Modern Standard Arabic (MSA) [Habash and Rambow 2005;Diab et al 2007;Larkey et al 2007;Buckwalter 2007;Dichy and Farghaly 2007]. Significant recent attention, however, has been paid to the problems for NLP 1 See…”

mentioning

confidence: 97%

Introduction to the Special Issue on Arabic Computational Linguistics

Katz

Diab

2011

ACM Transactions on Asian Language Information Processing

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In recent years there has been a significant increase in the amount of research on the computational analysis of the Arabic language being carried out around the world. This includes both a steady growth of the computational linguistics research community in the Arab-speaking world, as evidenced by the emergence of scientific societies such as the Egyptian Society for Language Engineering 1 , as well as the existence of major initiatives on Arabic natural language processing (NLP) in Europe and North America (particularly in the United States by the DARPA-funded GALE program 2 ). In addition, the past year alone saw the publication of two monographs on Arabic Computational Linguistics [Farghaly 2010; Habash 2010], a special issue of this journal [Shaalan and Farghaly 2009], a new release by the Linguistics Data Consortium of the Arabic Treebank [Maamouri et al. 2009] (among other Arabic-language resources), as well as a number of conferences and workshops on Arabic NLP around the world.At the 2010 Georgetown University Roundtable on Language 3 (GURT), whose theme was Arabic Language and Linguistics, linguists from around the world and from a wide range of different linguistic traditions and sub-disciplines came together in Washington, D.C. to share their research on the Arabic language. A two-day special session dedicated to Arabic computational linguistics and a half-day preconference tutorial on Arabic NLP drew researchers from both academia and industry. The presentations in the special session spanned the range from applied NLP to natural language understanding, from automatic diacritization to speech act classification, providing attendees with a snapshot of the current state of Arabic NLP. The papers in this volume were selected from those presentations to represent the different approaches and techniques currently being used in the field of Arabic computational linguistics.Arabic NLP research faces a number of well-known challenges, due to both the morphological complexity of the Arabic language and to the particularities of Arabic orthography. These challenges have been addressed extensively in Arabic NLP research which has focused on Modern Standard Arabic (MSA) [Habash and Rambow 2005;Diab et al. 2007;Larkey et al. 2007;Buckwalter 2007;Dichy and Farghaly 2007]. Significant recent attention, however, has been paid to the problems for NLP 1 See

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Grammar-Lexis Relations in the Computational Morphology of Arabic

Cited by 5 publications

References 8 publications

Arabic Morphological Analysis Techniques

Arabic Morphological Analysis Techniques

A Computational Analysis of Arabic Noun Morphology

Introduction to the Special Issue on Arabic Computational Linguistics

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