2009 CCPR Keynotes

Author

Uszkoreit, H.

Author_Institution

Saarland Univ., Saarbrucken, Germany

fYear

2009

fDate

4-6 Nov. 2009

Abstract

Minimally supervised machine learning methods based on bootstrapping are an attractive approach to advanced information extraction. Complex patterns signalling relevant semantic relations in free texts can be detected in this way. However, the potential and limitations of such methods are not yet sufficiently understood. We have systematically analyzed a bootstrapping approach. The starting point of the analysis is a pattern-learning graph, which is a subgraph of the bipartite graph representing all connections between linguistic patterns and relation instances exhibited by the data. It is shown that the performance of such general learning framework for actual tasks is dependent on certain properties of the data and on the seed construction. However, the greatest improvements can be obtained through the systematic learning of negative patterns.

Keywords

computational linguistics; graph theory; information retrieval; learning (artificial intelligence); text analysis; bipartite subgraph; bootstrapping approach; free text detection; information extraction; minimally supervised machine learning method; positive-negative pattern learning graph; relation extraction; seed construction; semantic relation;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2009. CCPR 2009. Chinese Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4244-4199-0

Type

conf

DOI

10.1109/CCPR.2009.5344160

Filename

5344160