Classifying the Hungarian web

Authors:
András Kornai;Marc Krellenstein;Michael Mulligan;David Twomey;Fruzsina Veress;Alec Wysoker
Affiliations:
Metacarta Inc., Cambridge, MA;Reed-Elsevier Inc., Burlington, MA;divine Inc., Burlington, MA;CEHQ, Inc., Needham, MA;Teragram Corp., Boston, MA;deNovis Inc., One Cranberry Hill, Lexington, MA
Venue:
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Year:
2003

Citing 5
Cited 3

On relevance weights with little relevance information

Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A hidden Markov model information retrieval system

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

Language Identification on the Web: Extending the Dictionary Method

CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Blog categorization exploiting domain dictionary and dynamically estimated domains of unknown words

HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Web-based frequency dictionaries for medium density languages

WAC '06 Proceedings of the 2nd International Workshop on Web as Corpus

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present some lessons learned from building vizsla, the keyword search and topic classification system used on the largest Hungarian portal, [origo.hu]. Based on a simple statistical language, model, and the large-scale supporting evidence from vizsla, we argue that in topic classification only positive evidence matters.