Spectral Graph Convolutional Networks for Part-of-Speech Tagging
- Part-of-Speech tagging is the process of assigning words with similar grammatical properties to a part of speech (PoS). In the English language, PoS-tagging algorithms generally reach very high accuracy. This thesis undertakes the task to test against these accuracies in PoS-tagging as a qualitative measure in classification capabilities for a recently developed neural network model, called graph convolutional network (GCN). The novelty proposed in this thesis is to translate a corpus into a graph as a direct input for the GCN. The experiments in this thesis serve as a proof of concept with room for improvements.
Author: | Saner Demirel |
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URN: | urn:nbn:de:kola-15080 |
Advisor: | Steffen Staab, Lukas Schmelzeisen |
Document Type: | Bachelor Thesis |
Language: | English |
Date of completion: | 2017/02/10 |
Date of publication: | 2017/10/05 |
Publishing institution: | Universität Koblenz, Universitätsbibliothek |
Granting institution: | Universität Koblenz, Fachbereich 4 |
Release Date: | 2017/10/05 |
Number of pages: | v, 30 |
Institutes: | Fachbereich 4 / Institut für Computervisualistik |
Licence (German): | Es gilt das deutsche Urheberrecht: § 53 UrhG |