A WEAKLY SUPERVISED APPROACH FOR LARGE-SCALE RELATION EXTRACTION

Ludovic Jean-Louis, Romaric Besançon, Olivier Ferret, Adrien Durand

2011

Abstract

Standard Information Extraction (IE) systems are designed for a specific domain and a limited number of relations. Recent work has been undertaken to deal with large-scale IE systems. Such systems are characterized by a large number of relations and no restriction on the domain, which makes difficult the definition of manual resources or the use of supervised techniques. In this paper, we present a large-scale IE system based on a weakly supervised method of pattern learning. This method uses pairs of entities known to be in relation to automatically extract example sentences from which the patterns are learned. We present the results of this system on the data from the KBP task of the TAC 2010 evaluation campaign.

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Paper Citation


in Harvard Style

Jean-Louis L., Besançon R., Ferret O. and Durand A. (2011). A WEAKLY SUPERVISED APPROACH FOR LARGE-SCALE RELATION EXTRACTION . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011) ISBN 978-989-8425-79-9, pages 94-103. DOI: 10.5220/0003661200940103


in Bibtex Style

@conference{kdir11,
author={Ludovic Jean-Louis and Romaric Besançon and Olivier Ferret and Adrien Durand},
title={A WEAKLY SUPERVISED APPROACH FOR LARGE-SCALE RELATION EXTRACTION},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)},
year={2011},
pages={94-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003661200940103},
isbn={978-989-8425-79-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)
TI - A WEAKLY SUPERVISED APPROACH FOR LARGE-SCALE RELATION EXTRACTION
SN - 978-989-8425-79-9
AU - Jean-Louis L.
AU - Besançon R.
AU - Ferret O.
AU - Durand A.
PY - 2011
SP - 94
EP - 103
DO - 10.5220/0003661200940103