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Neha Sahu, Rizwan Arif,
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- Research Scholar, Assistant Professor Lingaya’s Vidyapeeth, Faridabad, Lingaya’s Vidyapeeth, Faridabad Haryana, Haryana India, India
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Abstract
nMachine learning has revolutionized various aspects of information retrieval, including key phrase extraction in digital libraries. Key phrase extraction is crucial for summarizing and categorizing vast amounts of textual data, enabling efficient search and retrieval processes. This study explores the application of machine learning techniques for automatic key phrase extraction in digital libraries. We review various supervised and unsupervised learning algorithms, including deep learning models, that are employed to identify and extract key phrases from academic papers, books, and other digital documents. Specifically, algorithms such as support vector machines (SVM), neural networks, and BERT (Bidirectional Encoder Representations from Transformers) are examined for their effectiveness in this task. The research highlights the importance of feature selection, dataset quality, and algorithm performance in achieving accurate and meaningful key phrase extraction. We also discuss the integration of natural language processing (NLP) tools and the challenges associated with multilingual and domain-specific libraries. Experimental results demonstrate the effectiveness of machine learning models in enhancing the accessibility and discoverability of digital content, ultimately contributing to the advancement of digital library services. Key phrase extraction is a crucial task in managing and utilizing the vast amounts of information stored in digital libraries. By automating the identification of key phrases, it becomes possible to enhance information retrieval, indexing, and summarization processes. This paper explores the application of machine learning techniques to key phrase extraction in digital libraries. We review various supervised and unsupervised learning methods, highlighting their strengths and weaknesses in different contexts. Supervised approaches, such as decision trees, support vector machines (SVM), and neural networks, as well as advanced models like BERT, require annotated datasets and can achieve high accuracy
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Keywords: Machine learning, Digital libraries, Text mining, Information retrieval, Deep learning
n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Cheminformatics(ijci)]
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| Volume | 01 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | June 20, 2024 | |
| Accepted | June 26, 2024 | |
| Published | July 19, 2024 |
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