Human-competitive tagging using automatic keyphrase extraction
O. Medelyan, E. Frank, and I. Witten. Internat. Conference of Empirical Methods in Natural Language Processing, EMNLP-2009,, (2009)
Abstract
This paper connects two research areas: automatic
tagging on the web and statistical keyphrase
extraction. First, we analyze the quality
of tags in a collaboratively created folksonomy
using traditional evaluation techniques. Next,
we demonstrate how documents can be tagged
automatically with a state-of-the-art keyphrase
extraction algorithm, and further improve performance
in this new domain using a new algorithm,
” Maui”, that utilizes semantic information
extracted from Wikipedia. Maui outperforms
existing approaches and extracts tags
that are competitive with those assigned by the
best performing human taggers.
Description
CiteULike: Human-competitive tagging using automatic keyphrase extraction
%0 Conference Paper
%1 Medelyan09mauiRecommendation
%A Medelyan, O.
%A Frank, E.
%A Witten, I. H.
%B Internat. Conference of Empirical Methods in Natural Language Processing, EMNLP-2009,
%D 2009
%K 09 Medelyan content maui recommendation tag
%T Human-competitive tagging using automatic keyphrase extraction
%U http://www.cs.waikato.ac.nz/\~ihw/papers/09-OM-EF-IHW-Humancompetitive\%20tag.pdf
%X This paper connects two research areas: automatic
tagging on the web and statistical keyphrase
extraction. First, we analyze the quality
of tags in a collaboratively created folksonomy
using traditional evaluation techniques. Next,
we demonstrate how documents can be tagged
automatically with a state-of-the-art keyphrase
extraction algorithm, and further improve performance
in this new domain using a new algorithm,
” Maui”, that utilizes semantic information
extracted from Wikipedia. Maui outperforms
existing approaches and extracts tags
that are competitive with those assigned by the
best performing human taggers.
@inproceedings{Medelyan09mauiRecommendation,
abstract = {This paper connects two research areas: automatic
tagging on the web and statistical keyphrase
extraction. First, we analyze the quality
of tags in a collaboratively created folksonomy
using traditional evaluation techniques. Next,
we demonstrate how documents can be tagged
automatically with a state-of-the-art keyphrase
extraction algorithm, and further improve performance
in this new domain using a new algorithm,
” Maui”, that utilizes semantic information
extracted from Wikipedia. Maui outperforms
existing approaches and extracts tags
that are competitive with those assigned by the
best performing human taggers.},
added-at = {2010-10-28T05:20:30.000+0200},
author = {Medelyan, O. and Frank, E. and Witten, I. H.},
biburl = {https://www.bibsonomy.org/bibtex/2a006e673430e3a04f102a0d6f649d77e/lee_peck},
booktitle = {Internat. Conference of Empirical Methods in Natural Language Processing, EMNLP-2009,},
citeulike-article-id = {4956431},
citeulike-linkout-0 = {http://www.cs.waikato.ac.nz/\~{}ihw/papers/09-OM-EF-IHW-Humancompetitive\%20tag.pdf},
description = {CiteULike: Human-competitive tagging using automatic keyphrase extraction},
interhash = {29798a229305c69309a4ac73d7fb56ac},
intrahash = {a006e673430e3a04f102a0d6f649d77e},
keywords = {09 Medelyan content maui recommendation tag},
posted-at = {2009-12-04 10:25:25},
priority = {2},
timestamp = {2010-10-28T05:20:30.000+0200},
title = {Human-competitive tagging using automatic keyphrase extraction},
url = {http://www.cs.waikato.ac.nz/\~{}ihw/papers/09-OM-EF-IHW-Humancompetitive\%20tag.pdf},
year = 2009
}