Analysis of AI-Based Research Paper Keyword Extraction Models in Umaru Musa Yar'adua University, Katsina, Katsina State
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Background of the Study
In academic research, the ability to identify relevant keywords plays a critical role in organizing and categorizing research papers, improving searchability, and enhancing citation indexing. Traditional methods of keyword extraction involve manual identification, which is time-consuming and prone to human error. With the rise of artificial intelligence (AI) techniques, particularly in natural language processing (NLP), automated keyword extraction has gained attention for its potential to streamline the process. AI-based models can analyze the content of research papers and accurately extract keywords that best represent the themes and subjects of the paper. This study explores AI-based keyword extraction models for academic research papers at Umaru Musa Yar'adua University, Katsina, Katsina State, to improve the organization and discoverability of research.
Statement of the Problem
At Umaru Musa Yar'adua University, the process of extracting keywords from research papers remains largely manual, which leads to inefficiencies and inconsistent results. Manual keyword extraction also limits the ability to quickly categorize research papers, making it difficult to identify relevant literature in a timely manner. AI-based keyword extraction models have the potential to automate this process, improving accuracy and efficiency. However, there is limited research on the effectiveness of AI-based keyword extraction models in the context of academic papers at this university. This study aims to fill this gap by developing and analyzing AI models that can automate keyword extraction for academic research.
Objectives of the Study
1. To design and implement AI-based models for automatic keyword extraction from research papers at Umaru Musa Yar'adua University.
2. To evaluate the performance and accuracy of the AI-based keyword extraction models compared to traditional manual methods.
3. To assess the feasibility and effectiveness of implementing AI-based keyword extraction models for academic research at the university.
Research Questions
1. How accurate are the AI-based models in extracting relevant keywords from academic research papers?
2. How do AI-based models for keyword extraction compare with traditional manual methods in terms of efficiency and accuracy?
3. What is the potential impact of AI-based keyword extraction models on research organization and discoverability at Umaru Musa Yar'adua University?
Research Hypotheses
1. The AI-based keyword extraction models will outperform traditional manual methods in terms of accuracy and efficiency.
2. The AI-based models will accurately extract relevant keywords that best represent the content of research papers.
3. Implementing AI-based keyword extraction models will significantly improve the organization and discoverability of research papers at the university.
Significance of the Study
This study will contribute to improving the management and discoverability of academic research at Umaru Musa Yar'adua University by applying AI technologies to streamline the keyword extraction process. The results could be used to enhance the academic research infrastructure at the university and serve as a model for other institutions.
Scope and Limitations of the Study
The study will focus on the development and evaluation of AI-based keyword extraction models for academic papers at Umaru Musa Yar'adua University. Limitations include the availability of research papers for analysis and the challenge of integrating the AI models into the university’s existing systems.
Definitions of Terms
• AI-Based Keyword Extraction: The process of using artificial intelligence models, particularly in natural language processing, to automatically identify key terms or keywords in a text.
• Research Paper: An academic written work that presents original research findings or literature reviews in a particular field of study.
• Natural Language Processing (NLP): A branch of AI that focuses on the interaction between computers and human language, enabling machines to process and understand text.
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