Automatic Labeling of Topics

Author

Magatti, Davide ; Calegari, Silvia ; Ciucci, Davide ; Stella, Fabio

Author_Institution

Dept. of Inf., Syst. & Commun., Univ. degli Studi di Milano-Bicocca, Milan, Italy

fYear

2009

fDate

Nov. 30 2009-Dec. 2 2009

Firstpage

1227

Lastpage

1232

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 topic 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.

Keywords

dictionaries; information analysis; thesauri; Google directory service; OpenOffice English Thesaurus; automatic labeling; dictionary; document corpus; similarity measures; topic labeling rules; Clustering algorithms; Data mining; Dictionaries; Informatics; Intelligent systems; Labeling; Ontologies; Probability distribution; Sampling methods; Thesauri; Automatic Topic Labeling; Latent Dirichlet Allocation; Topics Tree;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on

Conference_Location

Pisa

Print_ISBN

978-1-4244-4735-0

Electronic_ISBN

978-0-7695-3872-3

Type

conf

DOI

10.1109/ISDA.2009.165

Filename

5364126