This thesis focuses on unsupervised morphological seg- mentation, the fundamental task in NLP which aims to break words into morphemes. I describe and re-implement a model proposed in Lee et al. (2011) and evaluate it on 4 languages. Moreover, I present a generative model that could use word representation as extra fea- tures. The word representations are leant in unsupervised manner using neural language model. The experiment shows that using extra features improves the performance of the unsupervised model.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:305133 |
| Date | January 2012 |
| Creators | Tran, Manh-Ke |
| Contributors | Zeman, Daniel, Vidová Hladká, Barbora |
| 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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