Enhancing Arabic-text feature extraction utilizing label-semantic augmentation in few/zero-shot learning

Article Computer Science, Artificial Intelligence

Arabic question answering system: a survey

Tahani H. Alwaneen et al.

Summary: Question answering is a subfield of information retrieval that aims to answer questions posed in natural language. The development of Arabic question answering systems has been hindered by linguistic challenges and limited resources. Research in this area includes examining challenges, existing systems, techniques, and future directions for Arabic question answering systems.

ARTIFICIAL INTELLIGENCE REVIEW (2022)

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Article Computer Science, Artificial Intelligence

Improving Zero-Shot Learning Baselines with Commonsense Knowledge

Abhinaba Roy et al.

Summary: This paper proposes a method for improving zero-shot learning by using explicit relations defined in ConceptNet to generate commonsense embeddings, and combining them with existing semantic embeddings. Experimental results demonstrate the effectiveness of this approach.

COGNITIVE COMPUTATION (2022)

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Article Computer Science, Artificial Intelligence

Arabic sentiment analysis using dependency-based rules and deep neural networks

Arwa Diwali et al.

Summary: This research proposes a framework that combines Arabic dependency-based rules and deep learning models for sentiment analysis. The dependency-based rules map the meaning of words to concepts in the dependency structure of a sentence using linguistic patterns, and examining the dependent words reveals the general sentiment. The performance results show that the proposed framework outperforms state-of-the-art approaches in Arabic sentiment analysis.

APPLIED SOFT COMPUTING (2022)

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Article Computer Science, Information Systems

A comparative study of effective approaches for Arabic sentiment analysis

Ibrahim Abu Farha et al.

Summary: This study conducted a comprehensive comparative analysis of the most effective methods used for Arabic sentiment analysis, demonstrating the superior performance of transformer-based language models. The research also highlighted the limitations of existing annotated Arabic SA datasets and the challenge of sarcasm prevalent in Arabic dialects.

INFORMATION PROCESSING & MANAGEMENT (2021)

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Article Chemistry, Multidisciplinary