A distributed meta-learning system for Chinese entity relation extraction

Entity relation extraction is an important task for obtaining useful information from multiple text documents. This paper presents a distributed meta-learning method which incorporates the distributed system and the meta-learning strategy for Chinese entity relation extraction. At the basic level of the meta-learning, we construct a learner for each relation type and the basic learners are different with each other on account of different feature sets. Then the communication among these basic learners is set up to improve the performance. At the meta-level, the meta-learner is used to make decision based on the results of each basic learner. Experiments are carried out on Automatic Content Extraction Relation Detection and Characterization (ACE RDC) 2005 Chinese corpus and the results show that the -score of our distributed meta-learning system is 69.81%, which is higher than that of baseline (the method based on Support Vector Machine (SVM) using composite kernel) by 1.31% and precedes over the state-of-the-art systems.