Harnessing Truth Discovery Algorithms On The Topic Labelling Problem
Ngurah Agus Sanjaya ER, Mouhamadou Ba, Talel Abdessalem, Stéphane Bressan · 2018
Topics in topic modelling approaches are represented as a collection of weighted words. The labels for the topics, however, are not clearly defined and must be interpreted manually. Topic labelling proposes to automatically label the topics by leveraging a knowledge base or applying data mining and machine learning algorithms. We propose a naive topic labelling approach where we transform the labeling problem into selecting the best label for each word in the topic. The candidate labels are generated by querying a knowledge base using the top-N words of each topic. We construct a heterogeneous graph of topics, words, articles and candidate labels. To rank the candidate labels, we apply truth discovery algorithms on the graph. The performance evaluation using popular topic modelling datasets shows that the approach receives satisfactory accuracy.