Computational analysis of morphosyntactic categories in Georgian
This thesis describes the development of part-of-speech tagging resources for the Georgian language, consisting of i.) a new morphosyntactic language model for part-of-speech (POS) tagging purposes; ii.) tagging guidelines for tagging and post-editing; iii.) the KATAG tagset and iv.) the trained parameter files the probabilistic TreeTagger program needs to work on Georgian texts. A new morphosyntactic model of Georgian for part-of-speech tagging purposes is described in the thesis. The thesis also describes a tagset (KATAG) defined in accordance with a new morphosyntactic model of the language and a set of design principles and tagging guidelines. A stochastic methodology is used here to perform tagging in Georgian. Namely, the Treetagger - a probabilistic part-of-speech tagging program has been trained on Georgian texts. The justification for this choice is discussed. I use two tokenisation approaches in part-of-speech tagging. An accuracy of 92.41% using an enclitic tokenisation approach and accuracy of 87.13% was achieved using a non-enclitic tokenisation approach, corroborating my hypothesis that treating enclitic elements separately from the host words results in better tagging performance. To make the
Daraselia, Sophiko
Computational analysis of morphosyntactic categories in Georgian. This thesis describes the development of part-of-speech tagging resources for the Georgian language, consisting of i.) a new morphosyntactic language model for part-of-speech (POS) tagging purposes; ii.) tagging guidelines for tagging and post-editing; iii.) the KATAG tagset and iv.) the trained parameter files the probabilistic TreeTagger program needs to work on Georgian texts. A new morphosyntactic model of Georgian for part-of-speech tagging purposes is described in the thesis. The thesis also describes a tagset (KATAG) defined in accordance with a new morphosyntactic model of the language and a set of design principles and tagging guidelines. A stochastic methodology is used here to perform tagging in Georgian. Namely, the Treetagger - a probabilistic part-of-speech tagging program has been trained on Georgian texts. The justification for this choice is discussed. I use two tokenisation approaches in part-of-speech tagging. An accuracy of 92.41% using an enclitic tokenisation approach and accuracy of 87.13% was achieved using a non-enclitic tokenisation approach, corroborating my hypothesis that treating enclitic elements separately from the host words results in better tagging performance. To make the
computational analysis morphosyntactic category georgian describes development part speech tag resource georgian language consist new morphosyntactic language model part speech purpose tag guideline tag post_editing iii train parameter file probabilistic program need work georgian text new morphosyntactic model georgian part speech purpose describe thesis also describe define accordance new morphosyntactic model language set design principle tag guideline stochastic use perform tag georgian namely probabilistic part speech tag program train georgian text justification choice discuss use approach part speech accuracy use accuracy achieve use non corroborate hypothesis treat element separately host word well tag performance make