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Clustering of Distributed Word Representations and its Applicability for Enterprise Search

Machine learning of distributed word representations with neural embeddings is a state-of-the-art approach to modelling semantic relationships hidden in natural language. The thesis “Clustering of Distributed Word Representations and its Applicability for Enterprise Search” covers different aspects of how such a model can be applied to knowledge management in enterprises. A review of distributed word representations and related language modelling techniques, combined with an overview of applicable clustering algorithms, constitutes the basis for practical studies. The latter have two goals: firstly, they examine the quality of German embedding models trained with gensim and a selected choice of parameter configurations. Secondly, clusterings conducted on the resulting word representations are evaluated against the objective of retrieving immediate semantic relations for a given term. The application of the final results to company-wide knowledge management is subsequently outlined by the example of the platform intergator and conceptual extensions."

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa.de:bsz:14-qucosa-208869
Date04 October 2016
CreatorsKorger, Christina
ContributorsTechnische Universität Dresden, Fakultät Informatik, Dr. Birgit Demuth, Dr. Uwe Crenze, Dr. Birgit Demuth, Prof. Dr. rer. nat. habil. Uwe Aßmann
PublisherSaechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typedoc-type:masterThesis
Formatapplication/pdf

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