A WEAKLY SUPERVISED APPROACH FOR LARGE-SCALE RELATION EXTRACTION

被引：0

作者：

Jean-Louis, Ludovic ^{[1
]}

Besancon, Romaric ^{[1
]}

Ferret, Olivier ^{[1
]}

Durand, Adrien ^{[1
]}

机构：

[1] CEA, LIST, Vis & Content Engn Lab, F-92265 Fontenay Aux Roses, France

来源：

KDIR 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND INFORMATION RETRIEVAL | 2011年

关键词：

Information extraction; Relation extraction;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：94 / 103

页数：10

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