Master's Thesis von Markus Müller

With the rise of digitalization, information retrieval has to cope with increasing amounts of digitized content. Legal content providers invest a lot of money for building domain- specific ontologies such as thesauri to retrieve a significantly increased number of relevant documents. Since 2002, many label propagation methods have been developed e.g. to identify groups of similar nodes in graphs. Label propagation is a family of graph-based semi-supervised machine learning algorithms. In this thesis, we will test the suitability of label propagation methods to extend a thesaurus from the tax law domain. The graph on which label propagation operates is a similarity graph constructed from word embeddings. We cover the process from end to end and conduct several parameter-studies to understand the impact of certain hyper-parameters on the overall performance. The results are then evaluated in manual studies and compared with a baseline approach.

Keywords: Thesaurus Extension, Legal Tech, Information Retrieval, Label Propagation, Word Embeddings, Data Science, Machine Learning

Code Repository

GitHub: sebischair/ThesaurusLabelPropagation