Morphological System For Under-Resourced Languages Using Hybrid Approach

Computational morphology covers the automatic analysis (recognition of the internal structure) and generation (formation of a word) of words. As such, it is an ineluctable step in many Natural Language Processing (NLP) applications. Over the last thirty years, the computational morphology area has b...

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Main Author:	Saee, Suhaila
Format:	Thesis
Published:	2016
Subjects:	QP Physiology
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id	my-mmu-ep.7188
record_format	uketd_dc
spelling	my-mmu-ep.71882018-07-13T16:47:42Z Morphological System For Under-Resourced Languages Using Hybrid Approach 2016-10 Saee, Suhaila QP Physiology Computational morphology covers the automatic analysis (recognition of the internal structure) and generation (formation of a word) of words. As such, it is an ineluctable step in many Natural Language Processing (NLP) applications. Over the last thirty years, the computational morphology area has been dominated by the finite state approach. This approach makes use of finite state transducer as its internal representation to describe the morphological information at the lexical and surface levels. Finite state morphology has been claimed as a language-independent component and is capable of handling all complex languages such as Finnish, Turkish, and Arabic, including under-resourced languages (U-RL). However, its requirements for a large amount of linguistic resources, which are morphological rules, lexicon, and language experts, lead to its limitations. Indeed, these limitations have resulted in the issues of the U-RL in computational morphology. The issues comprise the morphological data acquisition, language morphology representation, and general rule formalism. Hence, a morphological system that would be able to overcome these issues is needed especially when dealing with U-RL. In this research, there are two main issues to be highlighted: i) a workflow of the morphological system that can be used with the U-RL and ii) the internal representation of morphological information that complies with the selected framework, that is the Structured String Tree Correspondence (SSTC). The aim of this research is to propose a new Structured String Tree Correpondence+Morphology (SSTC+M) framework for constructing a morphological system for U-RL. Three primary levels are designed in the proposed framework, namely, morphological data acquisition (level 1), morphology theory adaptation (level 2), and computational morphology adaptation (level 3). The output of each level will be the input to the next level. 2016-10 Thesis http://shdl.mmu.edu.my/7188/ http://library.mmu.edu.my/diglib/onlinedb/dig_lib.php phd doctoral Multimedia University Faculty of Computing and Informatics
institution	Multimedia University
collection	MMU Institutional Repository
topic	QP Physiology
spellingShingle	QP Physiology Saee, Suhaila Morphological System For Under-Resourced Languages Using Hybrid Approach
description	Computational morphology covers the automatic analysis (recognition of the internal structure) and generation (formation of a word) of words. As such, it is an ineluctable step in many Natural Language Processing (NLP) applications. Over the last thirty years, the computational morphology area has been dominated by the finite state approach. This approach makes use of finite state transducer as its internal representation to describe the morphological information at the lexical and surface levels. Finite state morphology has been claimed as a language-independent component and is capable of handling all complex languages such as Finnish, Turkish, and Arabic, including under-resourced languages (U-RL). However, its requirements for a large amount of linguistic resources, which are morphological rules, lexicon, and language experts, lead to its limitations. Indeed, these limitations have resulted in the issues of the U-RL in computational morphology. The issues comprise the morphological data acquisition, language morphology representation, and general rule formalism. Hence, a morphological system that would be able to overcome these issues is needed especially when dealing with U-RL. In this research, there are two main issues to be highlighted: i) a workflow of the morphological system that can be used with the U-RL and ii) the internal representation of morphological information that complies with the selected framework, that is the Structured String Tree Correspondence (SSTC). The aim of this research is to propose a new Structured String Tree Correpondence+Morphology (SSTC+M) framework for constructing a morphological system for U-RL. Three primary levels are designed in the proposed framework, namely, morphological data acquisition (level 1), morphology theory adaptation (level 2), and computational morphology adaptation (level 3). The output of each level will be the input to the next level.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Saee, Suhaila
author_facet	Saee, Suhaila
author_sort	Saee, Suhaila
title	Morphological System For Under-Resourced Languages Using Hybrid Approach
title_short	Morphological System For Under-Resourced Languages Using Hybrid Approach
title_full	Morphological System For Under-Resourced Languages Using Hybrid Approach
title_fullStr	Morphological System For Under-Resourced Languages Using Hybrid Approach
title_full_unstemmed	Morphological System For Under-Resourced Languages Using Hybrid Approach
title_sort	morphological system for under-resourced languages using hybrid approach
granting_institution	Multimedia University
granting_department	Faculty of Computing and Informatics
publishDate	2016
_version_	1747829663360614400

Morphological System For Under-Resourced Languages Using Hybrid Approach

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