The rise of Artificial Intelligence technology has raised concerns about the potential compromise of privacy due to the handling of personal data. Private AI prevents cybercrimes and falsehoods and protects human freedom and trust. While Federated Learning offers a solution by model training across decentralized devices or servers, thereby…

Digital systems are pervasive in modern societies -- augmenting personal and commercial driving, detecting cancer, and exploiting transitory events in financial markets. Attacks on these systems are growing in number, sophistication, and impact. Current cyber defenses are proving inadequate against some of these attacks. Cyber defense tools that…

During the last quarter century, the diffusion of information and communications technology (ICT) in Sub-Saharan Africa (SSA) has been shaped by noteworthy socioeconomic determinants and arguably resulted in significant socioeconomic impact. The first essay in this dissertation is an enquiry into the statistically significant socioeconomic…

The potential of speech technology to improve educational outcomes has been a topic of great interest in recent years. For example, automatic speech recognition (ASR) systems could be employed to provide kindergarten-aged children with real-time feedback on their literacy and pronunciation as they practice reading aloud. Within these systems,…

In light of the ever-increasing capability of computer technology and advancement in speech and natural language processing techniques, automated speech scoring of constructed responses is gaining popularity in many high-stakes assessment and low-stakes educational settings. Automated scoring is a highly interdisciplinary and complex subject, and…

This dissertation, which comprises three independent essays, explores the dynamics of online user behavior and provides IS policy implications across three different applications. The first essay employs an econometric empirical analysis to examine the role of IT interventions on online users' gambling behavior, based on field data collected over…

This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms,…

Treebanks are a necessary prerequisite for many NLP tasks, including, but not limited to, semantic role labeling. For many languages, however, treebanks are either nonexistent or too small to be useful. Time-critical applications may require rapid deployment of natural language software for a new critical language--much faster than the development…

Understanding the syntactic structure of a sentence is a necessary preliminary to understanding its semantics and therefore for many practical applications. The field of natural language processing has achieved a high degree of accuracy in parsing, at least in English. However, the syntactic structures produced by the most commonly used parsers…

One of the most critical steps to integrating heterogeneous e-Business applications using different XML schemas is schema matching, which is known to be costly and error-prone. Many automatic schema matching approaches have been proposed, but the challenge is still daunting because of the complexity of schemas and immaturity of technologies in…

In the current millennium, extensive use of computers and the internet caused an exponential increase in information. Few research areas are as important as information extraction, which primarily involves extracting concepts and the relations between them from free text. Limitations in the size of training data, lack of lexicons and lack of…

Firms now routinely collect information about the needs of their customers, but this information is not sufficiently considered during product design decisions. This research examines the relationship between designers and consumers to build an understanding of how the consumer should be represented to increase the consumer focus during the…

The goal of this dissertation is to introduce a method for deriving morphemes from Arabic words using stem patterns, a feature of Arabic morphology. The motivations are three-fold: modeling with morphemes rather than words should help address the out-of-vocabulary problem; working with stem patterns should prove to be a cross-dialectally valid…

Topic models like latent Dirichlet allocation (LDA) provide a framework for analyzing large datasets where observations are collected into groups. Although topic modeling has been fruitfully applied to problems social science, biology, and computer vision, it has been most widely used to model datasets where documents are modeled as exchangeable…

One of the problems associated with automatic thesaurus construction is with determining the semantic relationship between word pairs. Quasi-synonyms provide a type of equivalence relationship: words are similar only for purposes of information retrieval. Determining such relationships in a thesaurus is hard to achieve automatically. The term…