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1
Improving the Accessibility of Arabic Electronic Theses and Dissertations (ETDs) with Metadata and Classification
Abdelrahman, Eman. - : Virginia Tech, 2021
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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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3
Natural Language Processing Advancements By Deep Learning: A Survey ...
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4
Teaching Natural Language Processing through Big Data Text Summarization with Problem-Based Learning ; Data and Information Management
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5
A Framework for Hadoop Based Digital Libraries of Tweets
Bock, Matthew. - : Virginia Tech, 2017
Abstract: The Digital Library Research Laboratory (DLRL) has collected over 1.5 billion tweets for the Integrated Digital Event Archiving and Library (IDEAL) and Global Event Trend Archive Research (GETAR) projects. Researchers across varying disciplines have an interest in leveraging DLRL's collections of tweets for their own analyses. However, due to the steep learning curve involved with the required tools (Spark, Scala, HBase, etc.), simply converting the Twitter data into a workable format can be a cumbersome task in itself. This prompted the effort to build a framework that will help in developing code to analyze the Twitter data, run on arbitrary tweet collections, and enable developers to leverage projects designed with this general use in mind. The intent of this thesis work is to create an extensible framework of tools and data structures to represent Twitter data at a higher level and eliminate the need to work with raw text, so as to make the development of new analytics tools faster, easier, and more efficient. To represent this data, several data structures were designed to operate on top of the Hadoop and Spark libraries of tools. The first set of data structures is an abstract representation of a tweet at a basic level, as well as several concrete implementations which represent varying levels of detail to correspond with common sources of tweet data. The second major data structure is a collection structure designed to represent collections of tweet data structures and provide ways to filter, clean, and process the collections. All of these data structures went through an iterative design process based on the needs of the developers. The effectiveness of this effort was demonstrated in four distinct case studies. In the first case study, the framework was used to build a new tool that selects Twitter data from DLRL's archive of tweets, cleans those tweets, and performs sentiment analysis within the topics of a collection's topic model. The second case study applies the provided tools for the purpose of sociolinguistic studies. The third case study explores large datasets to accumulate all possible analyses on the datasets. The fourth case study builds metadata by expanding the shortened URLs contained in the tweets and storing them as metadata about the collections. The framework proved to be useful and cut development time for all four of the case studies. ; Master of Science
Keyword: big data; data structures; digital libraries
URL: http://hdl.handle.net/10919/78351
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6
Using Dependency Parses to Augment Feature Construction for Text Mining
Guo, Sheng. - : Virginia Tech, 2012
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7
Natural Language Toolkit (NLTK)
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8
Using Concept Maps as a Tool for Cross-Language Relevance Determination
Richardson, W. Ryan. - : Virginia Tech, 2007
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9
Update on the Networked Digital Library of Theses and Dissertations
Fox, Edward A.. - : Graduate School of Library Science, University of Illinois at Urbana-Champaign, 2000
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10
Incremental Clustering for Very Large Document Databases: Initial MARIAN Experience
In: Information sciences. - New York, NY : Elsevier Science Inc. 84 (1995) 1-2, 101-114
OLC Linguistik
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11
A query language for information graphs
Betrabet, Sangita. - : Virginia Tech, 1993
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12
Integrated Access to a Large Medical Literature Database
Fox, Edward A.; Koushik, Prabhakar M.; Chen, Qi-Fan. - : Department of Computer Science, Virginia Polytechnic Institute & State University, 1991
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13
Building a Lexicon from Machine-Readable Dictionaries for Improved Information Retrieval1
NUTTER, J. TERRY; FOX, EDWARD A.; EVENS, MARTHA W.. - : Oxford University Press, 1990
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14
Building a Lexicon from Machine-Readable Dictionaries for Improved Information Retrieval
Nutter, J. Terry; Fox, Edward A.; Evens, Martha W.. - : Department of Computer Science, Virginia Polytechnic Institute & State University, 1990
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15
A More Cost Effective Algorithm for Finding Perfect Hash Functions
Fox, Edward A.; Chen, Qi-Fan; Heath, Lenwood S.. - : Department of Computer Science, Virginia Polytechnic Institute & State University, 1988
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16
Creation of a Prolog Fact Base from the Collins English Dictionary
Wohlwend, Robert C.; Fox, Edward A.. - : Department of Computer Science, Virginia Polytechnic Institute & State University, 1988
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17
Development of the CODER System: A Test-bed for Artificial Intelligence Methods in Information Retrieval
Fox, Edward A.. - : Department of Computer Science, Virginia Polytechnic Institute & State University, 1986
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18
Building the CODER Lexicon: The Collins English Dictionary and its Adverb Definitions
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19
Building the CODER Lexicon: The Collins English Dictionary and Its Adverb Definitions
Fox, Edward A.; Wohlwend, Robert C.; Sheldon, Phyllis R.. - : Department of Computer Science, Virginia Polytechnic Institute & State University, 1986
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20
A Knowledge-Based System for Composite Document Analysis and Retrieval: Design Issues in the CODER Project
Fox, Edward A.; France, Robert K.. - : Department of Computer Science, Virginia Polytechnic Institute & State University, 1986
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