Improving the Automatic Text Classification Algorithm of Siav, a Case Study
Name
Geraldine King Granada
Abstract
Siav is an IT service company that provides products for electronic document management, workflow management and the preservation of digital documents. One of their projects is to create an automatic text classifier suitable for use in business contexts. The primary aim of this thesis is to improve the current accuracy and confidence reliability of the text classifier using neural networks. In order to accomplish these goals, the baselined implementation is analysed and a number of approaches from linguistic processing and neural networks are proposed to address limitations in the current technology. The proposed techniques are then implemented and the performance results are compared against the existing metrics. Finally, observations are made regarding the proposed solution and its suitability for business use compared to the existing one.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Fabrizio Maggi, PhD and Daniele Turato
Defence year
2018