MoL-2015-08: Mallinson, Jonathan (2015) Modelling Syntactic and Semantic Tasks with Linguistically Enriched Recursive Neural Networks. [Report]
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Abstract
In this thesis a compositional distributional semantic approach, the Recursive Neural Network, is used to syntactically-semantically compose non-symbolic representations of words. Unlike previous Recursive Neural Network models which use either no linguistic enrichment or significant symbolic syntactic enrichment, I propose minimal linguistic enrichments which are both semantic and syntactic. I achieve this by enriching the Recursive Neural Networks' models with core syntactic/semantic linguistic types: head, argument and adjunct. This approach brings together formal linguistics and computational linguistics, as such I give a broad account of these theories. The syntactic understanding of the model is tested by a parsing task and the semantic understanding is tested by a paraphrase detection task. The results of these tasks not only show the benefits of linguistic enrichment but also raise further questions of study.
| Item Type: | Report |
|---|---|
| Report Nr: | MoL-2015-08 |
| Series Name: | Master of Logic Thesis (MoL) Series |
| Year: | 2015 |
| Uncontrolled Keywords: | logic, language |
| Subjects: | Logic |
| Date Deposited: | 12 Oct 2016 14:38 |
| Last Modified: | 12 Oct 2016 14:38 |
| URI: | https://eprints.illc.uva.nl/id/eprint/949 |
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