In this thesis we describe a method of using associative networks for automatic doc- ument grouping. Associative networks are networks of ideas or concepts in which each concept is linked to concepts that are semantically similar to it. By activating concepts in the network based on the text of a document and spreading this activation to related con- cepts, we can determine which concepts are related to the document, even if the document itself does not contain words linked directly to those concepts. Based on this information, we can group documents by the concepts they refer to. In the first part of the thesis we describe the method itself, as well as the details of various algorithms used in the implementation. We additionally discuss the theory upon which the method is based and compare it to various related methods. In the second part of the thesis we evaluate techniques to create associative networks from easily accessible knowledge sources, as well as different methods for the training of the associative network. Additionally, we evaluate techniques to improve the extraction of concepts from documents, we compare methods of spreading activation from concept to concept, and we present a novel technique by which the extracted concepts can be used to categorize documents. We also extend the method of associative networks to enable application to multilingual document libraries and compare the method to other state-of- the-art methods for document grouping. Finally, we present a practical application of associative networks, as implemented in a corporate environment in the form of the Pagelink Knowledge Centre. We demonstrate the practical usability of our work, and discuss the various advantages and disadvantages that the method of associative networks offers.
|Award date||10 Jun 2015|
|Place of Publication||Enschede|
|Publication status||Published - 10 Jun 2015|
Bloom, N. (2015). Grouping by association: using associative networks for document categorization. Enschede: Centre for Telematics and Information Technology (CTIT). https://doi.org/10.3990/1.9789036538787