The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
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
The rapid development of Artificial Intelligence (AI), especially Deep Learning (DL) technologies, has brought unprecedented challenges and opportunities for Intellectual Property (IP) protection and management. In this…
The advent of Artificial Intelligence (AI) has dramatically altered Customer Relationship Management (CRM) by allowing organizations to anticipate customer behavior, customize interactions and automate service delivery.…
The disappearance of Indigenous languages results in a decrease in cultural diversity, hence making the preservation of these languages extremely important. Conventional methods of documentation are lengthy, and the pres…
The increasing use of electronic health records (EHRs) has led to a surge in unstructured data, making it challenging to extract valuable insights. This study proposes Natural Language Processing (NLP) based techniques t…
This research proposes a hybrid approach for Named-Entity Recognition (NER) for Setswana, a low-resource language, that combines a bidirectional long short-term memory (BiLSTM) with a transfer learning model and a convol…
The escalating complexity of modern software systems has rendered the management of requirements increasingly arduous, often plagued by redundancy, inconsistency, and inefficiency. Traditional manual methods prove inadeq…
As globalization accelerates, the threat of terrorist attacks poses serious challenges to national security and public safety. Traditional detection methods rely heavily on manual monitoring and rule-based surveillance,…
Error detection and correction is an important activity that ensures the quality of written communication, especially in education, business, and legal documentation. State-of-the-art NLP approaches have several issues,…
The integration of machine-aided learning into college English education offers transformative potential for enhancing teaching and learning outcomes. This paper investigates the application of computational models, incl…
In current era of globalization, English language learning is important as it has become a global language and helps people to communicate from various regions and languages. For vocational students whose main aim is to…
