(English) Vinit Ravishankar July 2018 The aim of this thesis is twofold; first, we attempt to dependency parse existing code-switched corpora, solely by training on monolingual dependency treebanks. In an attempt to do so, we design a dependency parser and ex- periment with a variety of methods to improve upon the baseline established by raw training on monolingual treebanks: these methods range from treebank modification to network modification. On this task, we obtain state-of-the- art results for most evaluation criteria on the task for our evaluation language pairs: Hindi/English and Komi/Russian. We beat our own baselines by a sig- nificant margin, whilst simultaneously beating most scores on similar tasks in the literature. The second part of the thesis involves introducing the relatively understudied task of predicting code-switching points in a monolingual utter- ance; we provide several architectures that attempt to do so, and provide one of them as our baseline, in the hopes that it should continue as a state-of-the-art in future tasks. 1
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:387870 |
| Date | January 2018 |
| Creators | Ravishankar, Vinit |
| Contributors | Zeman, Daniel, Mareček, David |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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