Integrating Semantic Features for Enhancing Arabic Named Entity Recognition

Alsayadi, Hamzah A.; El-Korany, Abeer

doi:10.14569/ijacsa.2016.070318

Cited by 5 publications

(6 citation statements)

References 19 publications

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“…One such method was searching for indicators. Using indicators [1,6] enhances the accuracy of NER. In the case of failure to classify through use of indicators, the classification model will refer to gazetteer lists.…”

Section: Methodsmentioning

confidence: 99%

“…In the case of failure to classify through use of indicators, the classification model will refer to gazetteer lists. Many researchers [1,4,6,18] use gazetteers to improve their works.…”

Section: Methodsmentioning

confidence: 99%

“…Hybrid technologies use a combination of rule-based and machine learning-based algorithms [2,6]. The empirical results indicate that the hybrid approach outperforms both the rule-based and the ML-based approaches when they are processed independently because it combines two technologies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Gazetteers and different types of indicators are used for classification. The authors developed an integrated semantic-based ML model [6] for enhancing ANER. The authors' idea was to combine several linguistic features and to use syntactic dependencies to infer semantic relationships between named entities.…”

Section: A Name Entity Recognition Systemsmentioning

confidence: 99%

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Visualization of Arabic Entities in Online Social Media using Machine Learning

Alyamani¹,

Saudagar²

2021

IJACSA

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In recent years, the use of social media and the amount of exchangeable data have increased considerably. The increase in exchangeable data makes data mining, analysis and visualization of relevant information a challenging task. This research work assesses, categorizes, and analyzes Arabic entities on social media selected by users at certain time intervals. To accomplish this aim, the authors built a highly efficient classification model to classify entities according to three categories: person, location, and organization. The developed model captures an entity and specific time, collects all the posts on tweeter that refer to the entity at this specific time, and then classifies, visualize the entity through three methods. It first starts with classifying the entity through a corpus model that depends on customized corpus. If the entity is not classified through that model, it will be send to an indicators model which uses the pre-indicators or post-indicators for classing. Finally, the entity is passed to a gazetteer model which searches for the entity in three gazetteers (person, location, and organization), and accordingly determines the number of times the entity reference is repeated. This work allows scholars and researchers in different fields to visualize the frequency of entities referenced by a community. It also compares how references to entities change over time. The experimental results show that accuracy of the developed model in classifying the tweets is nearly 90%.

show abstract

Section: Methodsmentioning

confidence: 99%

“…In the case of failure to classify through use of indicators, the classification model will refer to gazetteer lists. Many researchers [1,4,6,18] use gazetteers to improve their works.…”

Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: A Name Entity Recognition Systemsmentioning

confidence: 99%

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Visualization of Arabic Entities in Online Social Media using Machine Learning

Alyamani¹,

Saudagar²

2021

IJACSA

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“…Alsayadi & ElKorany (2016) presented a new model which depends on the machine learning approach [19]. This model aims to combine several linguistic features and to utilize syntactic dependencies to infer semantic relations between three named entities: person, organization and location.…”

Section: B Machine Learning-based Nermentioning

confidence: 99%

An Overview of Ontology Learning Process from Arabic Text

Muhammed

Azim

Gheith

2020

The Egyptian Journal of Language Engineering

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Ontology learning plays an important role in many fields especially in the Semantic Web. The success of the Semantic Web depends on the quality of its ontologies. There is a lot of research work interested in Ontology Learning for Arabic texts. Most of these works focused on three main issues: extracting the terms, extracting the semantic relations, and building the ontology from the Arabic text. In this paper, first, we present the Arabic challenges that were reasons for developing few Arabic Ontology Learning systems. Second, we make a research comparison based on the techniques used and their results. Third, we pointed out the limitations and comments of research works on Arabic Ontology Learning. Finally, we concluded the paper and outlined our future research direction in this area.

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Arabic speech recognition using deep learning methods: Literature review

Qasim¹,

Abdulbaqi²

2022

PROCEEDING OF THE 1ST INTERNATIONAL CONFERENCE ON ADVANCED RESEARCH IN PURE AND APPLIED SCIENCE (ICARPAS2021): Third Annual Con

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Integrating Semantic Features for Enhancing Arabic Named Entity Recognition

Cited by 5 publications

References 19 publications

Visualization of Arabic Entities in Online Social Media using Machine Learning

Visualization of Arabic Entities in Online Social Media using Machine Learning

An Overview of Ontology Learning Process from Arabic Text

Arabic speech recognition using deep learning methods: Literature review

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