Propagating fine-grained topic labels in news snippets

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Luís Sarmento (Author) Other; Sérgio Nunes (Author) FEUP; Jorge Teixeira (Author) FEUP; Eugénio Oliveira (Author) FEUP

Conference proceedings International

Title: 2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 2

Pages: 515-518

IEEE/WIC/ACM International Conferences on Web Intelligence (WI)/Intelligent Agent Technologies (IAT),

Milan, ITALY, SEP 15-18, 2009

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 2 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

Other information

Authenticus ID: P-003-SWF

DOI: 10.1109/wi-iat.2009.338

Abstract (EN): We propose an unsupervised method for propagating automatically extracted fine-grained topic labels among news items to improve their topic description for subsequent text classification procedure. This method compares vector representations of news items and assigns to each news item the label of its closest neighbour with a different topic label. Results obtained show that high precision can be achieved in propagating the top ranked topic label, and that 2-gram and 3-gram feature representations optimize the precision.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4