Figuring out the degree of semantic similarity between two sentences is important for many practical applications of natural language processing. The goal is to determine the similarity of sentences on a scale from "sentences are unrelated" to "sentences are equivalent". In this thesis we examined application of di erent neural network architectures to solve this problem. We proposed models based on Recurrent neural networks, which convert text sequence to constant sized vector. We followed up with suitable representation of unknown words. Our experiments showed that simple architectures achieved better results on the used dataset. We see a future extension of this thesis by using bigger training dataset. 1
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:355652 |
| Date | January 2017 |
| Creators | Hrinčár, Peter |
| Contributors | Kadlec, Rudolf, Helcl, Jindřich |
| Source Sets | Czech ETDs |
| Language | Slovak |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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