Semantic and stylistic text analysis and text summary evaluation

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master_Heuer_Hendrik_2015.pdf (1.94 MB)

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Perustieteiden korkeakoulu | Master's thesis
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Date

2015

Department

Major/Subject

Human Computer Interaction and Design

Mcode

SCI3020

Degree programme

Master's Programme in ICT Innovation

Language

en

Pages

4+42

Series

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.

Description

Supervisor

Kaski, Samuel

Thesis advisor

Laaksonen, Jorma

Keywords

text analysis, machine learning, distributional semantics, word representations, word2vec, dimensionality reduction

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https://urn.fi/URN:NBN:fi:aalto-201509184348

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[dipl] Perustieteiden korkeakoulu / SCI