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Improving the Accessibility of Arabic Electronic Theses and Dissertations (ETDs) with Metadata and Classification
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Abstract:
Much research work has been done to extract data from scientific papers, journals, and articles. However, Electronic Theses and Dissertations (ETDs) remain an unexplored genre of data in the research fields of natural language processing and machine learning. Moreover, much of the related research involved data that is in the English language. Arabic data such as news and tweets have begun to receive some attention in the past decade. However, Arabic ETDs remain an untapped source of data despite the vast number of benefits to students and future generations of scholars. Some ways of improving the browsability and accessibility of data include data annotation, indexing, parsing, translation, and classification. Classification is essential for the searchability and management of data, which can be manual or automated. The latter is beneficial when handling growing volumes of data. There are two main roadblocks to performing automatic subject classification on Arabic ETDs. The first is the unavailability of a public corpus of Arabic ETDs. The second is the Arabic languages linguistic complexity, especially in academic documents. This research presents the Otrouha project, which aims at building a corpus of key metadata of Arabic ETDs as well as providing a methodology for their automatic subject classification. The first goal is aided by collecting data from the AskZad Digital Library. The second goal is achieved by exploring different machine learning and deep learning techniques. The experiments results show that deep learning using pretrained language models gave the highest classification performance, indicating that language models significantly contribute to natural language understanding. ; M.S. ; An Electronic Thesis or Dissertation (ETD) is an openly-accessible electronic version of a graduate students research thesis or dissertation. It documents their main research effort that has taken place and becomes available in the University Library instead of a paper copy. Over time, collections of ETDs have been gathered and made available online through different digital libraries. ETDs are a valuable source of information for scholars and researchers, as well as librarians. With the digitalization move in most Middle Eastern Universities, the need to make Arabic ETDs more accessible significantly increases as their numbers increase. One of the ways to improve their accessibility and searchability is through providing automatic classification instead of manual classification. This thesis project focuses on building a corpus of metadata of Arabic ETDs and building a framework for their automatic subject classification. This is expected to pave the way for more exploratory research on this valuable genre of data.
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Keyword:
Arabic Electronic Theses and Dissertations (ETDs); Automatic Classification; Deep learning (Machine learning); Digital Libraries; Machine learning; NLP; Pretrained Language Models
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URL: http://hdl.handle.net/10919/107790
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| 2 |
Otrouha: A Corpus of Arabic ETDs and a Framework for Automatic Subject Classification ; The Journal of Electronic Theses and Dissertations
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Natural Language Processing Advancements By Deep Learning: A Survey ...
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Teaching Natural Language Processing through Big Data Text Summarization with Problem-Based Learning ; Data and Information Management
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Using Dependency Parses to Augment Feature Construction for Text Mining
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Using Concept Maps as a Tool for Cross-Language Relevance Determination
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Update on the Networked Digital Library of Theses and Dissertations
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Fox, Edward A.. - : Graduate School of Library Science, University of Illinois at Urbana-Champaign, 2000
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Building a Lexicon from Machine-Readable Dictionaries for Improved Information Retrieval1
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Building a Lexicon from Machine-Readable Dictionaries for Improved Information Retrieval
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| 15 |
A More Cost Effective Algorithm for Finding Perfect Hash Functions
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Creation of a Prolog Fact Base from the Collins English Dictionary
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Development of the CODER System: A Test-bed for Artificial Intelligence Methods in Information Retrieval
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Fox, Edward A.. - : Department of Computer Science, Virginia Polytechnic Institute & State University, 1986
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Building the CODER Lexicon: The Collins English Dictionary and its Adverb Definitions
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Building the CODER Lexicon: The Collins English Dictionary and Its Adverb Definitions
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A Knowledge-Based System for Composite Document Analysis and Retrieval: Design Issues in the CODER Project
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