An algorithm for the automatic labeling of topics, accordingly to a hierarchy, is presented. Its main ingredients are a set of similarity measures and a set of topics labeling rules. The labeling rules are specifically designed to find the most agreed labels between the given topic and the hierarchy. The hierarchy is obtained from the Google Directory service, extracted via an ad-hoc developed software procedure and expanded through the use of the OpenOffice English Thesaurus. The performance of the proposed algorithm is investigated by using a document corpus consisting of 33,801 documents and a dictionary consisting of 111,795 words. The results are encouraging while particularly interesting and significant labeling cases emerged.
Stella, F., Ciucci, D., Calegari, S., Magatti, D. (2009). Automatic labeling of topics. In An Efficient Combinatorial Approach for Solving the DNA Motif Finding Problem (pp.1227-1232). IEEE [10.1109/ISDA.2009.165].
Automatic labeling of topics
STELLA, FABIO ANTONIO;CIUCCI, DAVIDE ELIO;CALEGARI, SILVIA;MAGATTI, DAVIDE
2009
Abstract
An algorithm for the automatic labeling of topics, accordingly to a hierarchy, is presented. Its main ingredients are a set of similarity measures and a set of topics labeling rules. The labeling rules are specifically designed to find the most agreed labels between the given topic and the hierarchy. The hierarchy is obtained from the Google Directory service, extracted via an ad-hoc developed software procedure and expanded through the use of the OpenOffice English Thesaurus. The performance of the proposed algorithm is investigated by using a document corpus consisting of 33,801 documents and a dictionary consisting of 111,795 words. The results are encouraging while particularly interesting and significant labeling cases emerged.
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