Title :
2009 CCPR Keynotes
Author_Institution :
Saarland Univ., Saarbrucken, Germany
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;
Conference_Titel :
Pattern Recognition, 2009. CCPR 2009. Chinese Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4199-0
DOI :
10.1109/CCPR.2009.5344160
