Arabic Morphological Analysis and Disambiguation Using a Possibilistic Classifier

Ayed, Raja; Bounhas, Ibrahim; Elayeb, Bilel; Evrard, Fabrice; Saoud, Narjès Bellamine Ben

doi:10.1007/978-3-642-31576-3_36

Cited by 15 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the lack of standardized resources and poor collaboration between researchers, it is difficult to assess objectively these works. For recommendations, we believe that future projects should incorporate terminology extraction tools that allow a full morphological and disambiguation analysis such as MADA and the Ayed tool [12]. At the syntactic level, we recommend implementing a grammar of noun phrases, taking inspiration from works of Attia and Bounhas.…”

Section: Resultsmentioning

confidence: 98%

See 1 more Smart Citation

Arabic Domain Terminology Extraction: A Literature Review

Bounhas

Lahbib

Elayeb

2014

On the Move to Meaningful Internet Systems: OTM 2014 Conferences

Self Cite

View full text Add to dashboard Cite

Abstract. Domain terminology extraction is an important step in many applications such as ontology building and information retrieval. Analyzing a corpus to automatically extract key terms is a difficult task, especially in the case of Arabic language. The complexity of spelling, morphology and semantics of Arabic makes natural language processing tasks quite difficult. In addition to the complexity of Arabic, the challenges related to domain terminology extraction are caused by the inherent difficulty in determining whether a word or a phrase represents or not a given text. All these problems have not restricted the multitude of Arabic terminology extraction approaches in the ontology building process. Therefore, this article presents a literature review in the field of Arabic terminology extraction focusing on the specificities of this language.

show abstract

Section: Resultsmentioning

confidence: 98%

“…These tools return, in addition to POS, morphological attributes of a treated word such as gender and number for nouns and verbs mode. We name the MADA tool [11] and the tool of Ayed [12].…”

Section: Terminology and Ate Toolsmentioning

confidence: 99%

Arabic Domain Terminology Extraction: A Literature Review

Bounhas

Lahbib

Elayeb

2014

On the Move to Meaningful Internet Systems: OTM 2014 Conferences

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some features are binary and the value which begins with the letter 'N' means that this word does not contain this feature. However, POS, pronoun, person, voice, aspect, gender, number, case and mode have more than two possible values [23,24].…”

Section: Proposals and Outlinementioning

confidence: 99%

“…Arabic text disambiguation consists in classifying the different morphological features through a perfect or an imperfect training set [23,24]. Thus, we study classification approaches to disambiguate non-vocalized texts using vocalized texts for training.…”

Section: Proposals and Outlinementioning

confidence: 99%

“…Indeed, we consider the morphological features as classes. We use the same 14 morphological features of the disambiguation tool MADA [4][5][6], which are: POS (part-of-speech), conjunction, particle, determiner, pronoun, person, voice, aspect, gender, number, case, preposition, mode and adjective [23]. Preposition, conjunction, adjective, particle and determiner have binary values indicating if a word contains or not, respectively, a preposition, a conjunction, an adjective, a particle and a determiner.…”

Section: Proposals and Outlinementioning

confidence: 99%

See 1 more Smart Citation

A hybrid possibilistic approach for Arabic full morphological disambiguation

Bounhas

Ayed

Elayeb

et al. 2015

Data & Knowledge Engineering

Self Cite

View full text Add to dashboard Cite

A comprehensive review on Arabic word sense disambiguation for natural language processing applications

Kaddoura

Ahmed

Hemanth

2022

WIREs Data Min & Knowl

View full text Add to dashboard Cite

In communication, textual data are a vital attribute. In all languages, ambiguous or polysemous words' meaning changes depending on the context in which they are used. The ability to determine the ambiguous word's correct meaning is a Know-distill challenging task in natural language processing (NLP). Word sense disambiguation (WSD) is an NLP process to analyze and determine the correct meaning of polysemous words in a text. WSD is a computational linguistics task that automatically identifies the polysemous word's set of senses. Based on the context some word comes into view, WSD recognizes and tags the word to its correct priori known meaning. Semitic languages like Arabic have even more significant challenges than other languages since Arabic lacks diacritics, standardization, and a massive shortage of available resources.Recently, many approaches and techniques have been suggested to solve word ambiguity dilemmas in many different ways and several languages. In this review paper, an extensive survey of research works is presented, seeking to solve Arabic word sense disambiguation with the existing AWSD datasets.

show abstract

Arabic Morphological Analysis and Disambiguation Using a Possibilistic Classifier

Cited by 15 publications

References 6 publications

Arabic Domain Terminology Extraction: A Literature Review

Arabic Domain Terminology Extraction: A Literature Review

A hybrid possibilistic approach for Arabic full morphological disambiguation

A comprehensive review on Arabic word sense disambiguation for natural language processing applications

Contact Info

Product

Resources

About