An ontology-based topic evaluation method for enhancing information filtering
Hanh V. Nguyen, Yue Xu, Yuefeng Li · QUT ePrints (Queensland University of Technology) · 2018
Topic Modelling has been applied in many successful applications in data mining, text mining, machine learning and information filtering. The limitation is that the quality of topics generated from the input corpus is not always good because many topics contain intrusive and ambiguous words. Hence, topic evaluation to assess and to rank the topics is really important for good quality topics before applying those topics to text based applications. In this study, we proposed an ontology-based topic evaluation method for enhancing information filtering, named as TRbTCM. This new model assesses the quality of topics based on matching topic models with subject headings in Library Congress Subject Heading (LCSH) ontology.