Natural language processing (NLP) is the field of computer science focused on enabling computers to understand, interpret, and generate human language. Core tasks include tokenization, part-of-speech tagging, syntactic parsing, named entity recognition, machine translation, text summarization, question answering, and sentiment analysis. Early NLP systems relied on rule-based grammars and statistical language models; current approaches are dominated by transformer-based architectures and large language models pretrained on extensive text corpora and fine-tuned for specific tasks. Active research increasingly targets efficient attention mechanisms, including linear and sparse attention, to reduce the heavy compute and memory costs of standard transformers, alongside work on multilingual and low-resource languages, model bias, and factual reliability in generated text. Applications include chatbots and virtual assistants, automated document analysis, information extraction from unstructured text, and cross-lingual translation systems. As an open-access natural language processing journal (an NLP journal), IJACSA publishes research on language models and applied systems evaluated across multiple languages and domain-specific text corpora.
Published in International Journal of Advanced Computer Science and Applications (IJACSA)
· list last refreshed September 2026
Textual entailment recognition is one of the recent challenges of the Natural Language Processing (NLP) domain. Deep learning strategies are used in the work of text entailment instead of traditional Machine learning or…
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A reasonable amount of annotated data is required for fine-tuning pre-trained language models (PLM) on down-stream tasks. However, obtaining labeled examples for different language varieties can be costly. In this paper,…
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Data analytics has an interesting variant that aims to understand an entity's behavior. It is termed as diagnostic analytics, which answers “why type questions”. “Why type questions” find their applications in emotion cl…
There is an increasing demand for analyzing the contents of social media. However, the process of sentiment analysis in Arabic language especially Arabic dialects can be very complex and challenging. This paper presents…
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