A Deep syntactic parser for the Tamil language
dc.contributor.advisor | Dias G | |
dc.contributor.advisor | Butt M | |
dc.contributor.author | Sarveswaran K | |
dc.date.accept | 2022 | |
dc.date.accessioned | 2022 | |
dc.date.available | 2022 | |
dc.date.issued | 2022 | |
dc.description.abstract | 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. | en_US |
dc.identifier.accno | TH5064 | en_US |
dc.identifier.citation | Sarveswaran, K. (2022). A Deep syntactic parser for the Tamil language [Doctoral dissertation, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21176 | |
dc.identifier.degree | Doctor of Philosophy | en_US |
dc.identifier.department | Department of Computer Science and Engineering | en_US |
dc.identifier.faculty | Engineering | en_US |
dc.identifier.uri | http://dl.lib.uom.lk/handle/123/21176 | |
dc.language.iso | en | en_US |
dc.subject | DEEP SYNTACTIC PARSER | en_US |
dc.subject | MORPHOLOGICAL ANALYSE | en_US |
dc.subject | GRAMMAR-DRIVEN PARSER | en_US |
dc.subject | DATA-DRIVEN PARSER | en_US |
dc.subject | PART OF SPEECH TAGGER | en_US |
dc.subject | INFORMATION TECHNOLOGY -Dissertation | en_US |
dc.subject | COMPUTER SCIENCE -Dissertation | en_US |
dc.title | A Deep syntactic parser for the Tamil language | en_US |
dc.type | Thesis-Abstract | en_US |
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