@inproceedings{sumida-etal-2008-boosting,
title = "Boosting Precision and Recall of Hyponymy Relation Acquisition from Hierarchical Layouts in {W}ikipedia",
author = "Sumida, Asuka and
Yoshinaga, Naoki and
Torisawa, Kentaro",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Tapias, Daniel",
booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)",
month = may,
year = "2008",
address = "Marrakech, Morocco",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/618_paper.pdf",
abstract = "This paper proposes an extension of Sumida and Torisawas method of acquiring hyponymy relations from hierachical layouts in Wikipedia (Sumida and Torisawa, 2008). We extract hyponymy relation candidates (HRCs) from the hierachical layouts in Wikipedia by regarding all subordinate items of an item x in the hierachical layouts as xs hyponym candidates, while Sumida and Torisawa (2008) extracted only direct subordinate items of an item x as xs hyponym candidates. We then select plausible hyponymy relations from the acquired HRCs by running a filter based on machine learning with novel features, which even improve the precision of the resulting hyponymy relations. Experimental results show that we acquired more than 1.34 million hyponymy relations with a precision of 90.1{\%}.",
}
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<abstract>This paper proposes an extension of Sumida and Torisawas method of acquiring hyponymy relations from hierachical layouts in Wikipedia (Sumida and Torisawa, 2008). We extract hyponymy relation candidates (HRCs) from the hierachical layouts in Wikipedia by regarding all subordinate items of an item x in the hierachical layouts as xs hyponym candidates, while Sumida and Torisawa (2008) extracted only direct subordinate items of an item x as xs hyponym candidates. We then select plausible hyponymy relations from the acquired HRCs by running a filter based on machine learning with novel features, which even improve the precision of the resulting hyponymy relations. Experimental results show that we acquired more than 1.34 million hyponymy relations with a precision of 90.1%.</abstract>
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%0 Conference Proceedings
%T Boosting Precision and Recall of Hyponymy Relation Acquisition from Hierarchical Layouts in Wikipedia
%A Sumida, Asuka
%A Yoshinaga, Naoki
%A Torisawa, Kentaro
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Tapias, Daniel
%S Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08)
%D 2008
%8 May
%I European Language Resources Association (ELRA)
%C Marrakech, Morocco
%F sumida-etal-2008-boosting
%X This paper proposes an extension of Sumida and Torisawas method of acquiring hyponymy relations from hierachical layouts in Wikipedia (Sumida and Torisawa, 2008). We extract hyponymy relation candidates (HRCs) from the hierachical layouts in Wikipedia by regarding all subordinate items of an item x in the hierachical layouts as xs hyponym candidates, while Sumida and Torisawa (2008) extracted only direct subordinate items of an item x as xs hyponym candidates. We then select plausible hyponymy relations from the acquired HRCs by running a filter based on machine learning with novel features, which even improve the precision of the resulting hyponymy relations. Experimental results show that we acquired more than 1.34 million hyponymy relations with a precision of 90.1%.
%U http://www.lrec-conf.org/proceedings/lrec2008/pdf/618_paper.pdf
Markdown (Informal)
[Boosting Precision and Recall of Hyponymy Relation Acquisition from Hierarchical Layouts in Wikipedia](http://www.lrec-conf.org/proceedings/lrec2008/pdf/618_paper.pdf) (Sumida et al., LREC 2008)
ACL