Semantic and stylistic text analysis and text summary evaluation

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Laaksonen, Jorma Heuer, Hendrik 2015-09-18T08:34:02Z 2015-09-18T08:34:02Z
dc.description.abstract The main contribution of this Master's thesis is a novel way of doing text comparison using word vector representations (word2vec) and dimensionality reduction (t-SNE). This yields a bird’s-eye view of different text sources, including text summaries and their source material, and enables users to explore a text source like a geographical map. The main goal of the thesis was to support the quality control and quality assurance efforts of a company. This goal was operationalized and subdivided into several modules. In this thesis, the Topic and Topic Comparison modules are described. For each module, the state of the art in natural language processing and machine learning research was investigated and applied. The implementation section of this thesis discusses what each module does, how it relates to theory, how the module is implemented, the motivation for the chosen approach and self-criticism. The thesis also describes how to derive a text quality gold standard using machine learning. en
dc.format.extent 4+42
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Semantic and stylistic text analysis and text summary evaluation en
dc.type G2 Pro gradu, diplomityö en Perustieteiden korkeakoulu fi
dc.subject.keyword text analysis en
dc.subject.keyword machine learning en
dc.subject.keyword distributional semantics en
dc.subject.keyword word representations en
dc.subject.keyword word2vec en
dc.subject.keyword dimensionality reduction en
dc.identifier.urn URN:NBN:fi:aalto-201509184348
dc.programme.major Human Computer Interaction and Design fi
dc.programme.mcode HCID fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Kaski, Samuel
dc.programme Master's Programme in ICT Innovation fi

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