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Instance Classification using Co-Occurrences on the Web

by: Gijs Geleijnse, Jan Korst, and Viktor de Boer

In: Proc. of the ISWC2006 Workshop on Web Content Mining with Human Language Technologies (2006) .

Resources (URL, PDF, PS...)

URL:	http://orestes.ii.uam.es/workshop/3.pdf

We present a novel unsupervised approach to mapping artrelated instances such as music artists and painters to subjective categories like genre and style. We base our approach on co-occurrences of the two on the web, found with Google. The co-occurrences are found using three methods: by identifying the search engine counts, by analyzing Google excerpts found by querying patterns and by scanning full documents. Per instance, we use the same co-occurrence-based approach to find its nearest neighbors, i.e. the most related instances. These results can be combined in order to create a more reliable classification. We tested and compared the three methods on two different domains: mapping music artists to genres, and painters to art-styles. The results show that the use of related instances indeed improves the precision of the classification. Moreover, the methods with the lowest Google Complexity perform best.

BibTeX record

@inproceedings{GKB06,
  abstract = {We present a novel unsupervised approach to mapping artrelated
instances (such as music artists and painters) to subjective categories
like genre and style. We base our approach on co-occurrences of
the two on the web, found with Google. The co-occurrences are found
using three methods: by identifying the search engine counts, by analyzing
Google excerpts found by querying patterns and by scanning full
documents. Per instance, we use the same co-occurrence-based approach
to find its nearest neighbors, i.e. the most related instances. These results
can be combined in order to create a more reliable classification.
We tested and compared the three methods on two different domains:
mapping music artists to genres, and painters to art-styles. The results
show that the use of related instances indeed improves the precision of
the classification. Moreover, the methods with the lowest Google Complexity
perform best.},
  added-at = {2006-11-06T22:05:31.000+0100},
  author = {Geleijnse, Gijs and Korst, Jan and de Boer, Viktor},
  biburl = {http://www.bibsonomy.org/bibtex/2027513ba40abf3789d6e8872fbe07287/lysander07},
  booktitle = {Proc. of the ISWC2006 Workshop on Web Content Mining with Human Language Technologies},
  interhash = {e26e7cca0055f3042f172a70968aa724},
  intrahash = {027513ba40abf3789d6e8872fbe07287},
  keywords = {co-occurence WCMHLT2006 google datamining linguistics},
  timestamp = {2006-11-06T22:05:31.000+0100},
  title = {Instance Classification using Co-Occurrences on the Web},
  url = {http://orestes.ii.uam.es/workshop/3.pdf},
  year = 2006
}

Endnote record

%0 Conference Paper
%1 GKB06
%A Geleijnse, Gijs
%A Korst, Jan
%A de Boer, Viktor
%B Proc. of the ISWC2006 Workshop on Web Content Mining with Human Language Technologies
%D 2006
%K 
%T Instance Classification using Co-Occurrences on the Web
%U http://orestes.ii.uam.es/workshop/3.pdf
%X We present a novel unsupervised approach to mapping artrelated
instances (such as music artists and painters) to subjective categories
like genre and style. We base our approach on co-occurrences of
the two on the web, found with Google. The co-occurrences are found
using three methods: by identifying the search engine counts, by analyzing
Google excerpts found by querying patterns and by scanning full
documents. Per instance, we use the same co-occurrence-based approach
to find its nearest neighbors, i.e. the most related instances. These results
can be combined in order to create a more reliable classification.
We tested and compared the three methods on two different domains:
mapping music artists to genres, and painters to art-styles. The results
show that the use of related instances indeed improves the precision of
the classification. Moreover, the methods with the lowest Google Complexity
perform best.

BibSonomy :: publication ::

Instance Classification using Co-Occurrences on the Web

Resources (URL, PDF, PS...)

Abstract

BibTeX record

Endnote record