Supervised learning algorithms have been used for automatic text categoriza- tion with very good results. But supervised learning requires a large amount of manually labeled training data and this is a serious limitation for many practical applications. Keyword-based text categorization does not require manually la- beled training data and has therefore been presented as an attractive alternative to supervised learning. The aim of this study is to explore if there are other li- mitations for using keyword-based text categorization in industrial applications. This study also tests if a new lexical resource, based on the paradigmatic rela- tions between words, could be used to improve existing keyword-based text ca- tegorization algorithms. An industry motivated use case was created to measure practical applicability. The results showed that none of five examined algorithms was able to meet the requirements in the industrial motivated use case. But it was possible to modify one algorithm proposed by Liebeskind et.al. (2015) to meet the requirements. The new lexical resource produced relevant keywords for text categorization but there was still a large variance in the algorithm’s capaci- ty to correctly categorize different text categories. The categorization capacity was also generally too low to meet the requirements in many practical applica- tions. Further studies are needed to explore how the algorithm’s categorization capacity could be improved.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-222164 |
| Date | January 2016 |
| Creators | Karlsson, Vide |
| Publisher | KTH, Programvaruteknik och datorsystem, SCS |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0018 seconds