Doctor of Philosophy (Ph.D.)

Permanent URI for this collectionhttp://192.248.9.226/handle/123/12348

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Browsing Doctor of Philosophy (Ph.D.) by Subject "COMPUTER SCIENCE -Dissertation"

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    item: Thesis-Abstract
    A Deep syntactic parser for the Tamil language
    (2022) Sarveswaran K; Dias G; Butt M
    Natural Language Processing (NLP) applications have become integral to human life. A syntactic parser is a vital linguistic tool that shows syntactic relations between the words in a sentence. These may then be mapped to a tree, a graph, or a formal structure. Syntactic parsers are helpful for building other NLP applications. In addition, they help linguists to understand a language better and perform cross-lingual linguistic analysis. A syntactic parser that performs a deeper analysis and captures argumentative, attributive and coordinative relations between the words of a given sentence is called a deep syntactic parser. Tamil is considered a low-resourced language in terms of tools, applications, and resources available for others to use and build NLP applications or carry out linguistic analyses. Not many resources, such as treebanks and annotated corpora, or linguistic analysis tools such as POS taggers or morphological analysers, are publicly available for Tamil. Available off-the-shelf language-agnostic syntactic parsers show comparatively low performance because of the rich morphosyntactic properties of Tamil. This study elaborates on how I developed the first grammar-driven parser for Tamil, which uses the Lexical-Functional Grammar formalism, and a state-of-the-art data-driven parser using the Universal Dependencies framework. I have also proposed an approach to evaluate a syntactic parser’s syntactical coverage, experimented with transition-based and graph-based approaches, and for the first time, tried multi-lingual training to develop a data-driven parser for Tamil. A part of speech tagger, a morphological analyser cum generator, pre-processing tools, and treebanks are the other tools and resources I have developed to facilitate the development of the parsers. While all these tools give the current best score for their respective tasks, these resources are also available online for others to build upon. Moreover, the study also documents my contributions toward understanding different linguistic aspects of the Tamil language.
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    item: Thesis-Abstract
    RABAN - a software implementation process for robotic process automation (RPA) projects
    (2022) Padmini KVJ; Perera GIUS; Bandara HMND
    Robotic Process Automation (RPA), the next level of business process automation, provides adaptive and transformative solutions to replace timeconsuming, non-value-adding, and repetitive human tasks in a Business Process (BP). RPA based BP transformation projects differ from typical software development projects because RPA bots are developed on stable code. It is counterproductive to use existing software processes in RPA projects. A process template (i.e., software implementation process and metrics to track the project) is yet to be derived for RPA projects. The estimated initial RPA project failure rates are 30-50%, and the lack of a fitting implementation process is attributed as one of the key contributors to failure. We addressed this gap and derived a novel process for RPA projects named Raban and metrics to track RPA projects. Scrum was used to formulate the Raban. Focus group discussions were conducted with scrum teams and identified 80 challenges. Those analyzed in Straussian grounded theory are grouped into six categories (i.e., lack of agile mindset, inconsistency in story estimation, client management issues, lack of adherence to agile practices, scope change in requirement freeze, and lack of quantitative measurement). Prioritized 15 burning challenges were classified based on significance, and taxonomy was developed. Derived steps to estimate RPA use-cases and a framework to achieve customer satisfaction adopting design thinking practices in agile projects. Moreover, 17 software metrics and three artifacts were derived and validated in five scrum projects. Raban was derived based on the solutions identified and further fine-tuned based on the feedback from follow-up interviews with the stakeholders and two workshops conducted with the other RPA project teams. After that, 14 metrics and two artifacts were derived for Raban and validated in a RPA project. Moreover, to select the right candidate BP for RPA transformation, predictive machine learning model was developed, where the decision made as yes/no on RPA suitability. We used 16 factors and a two-class decision forest classification model to develop the model.