Active learning for relation type extension with local and global data views
Ang Sun, Ralph Grishman · 2012
Relation extraction is the process of identifying instances of specified types of semantic relations in text; relation type extension involves extending a relation extraction system to recognize a new type of relation. We present LGCo-Testing, an active learning system for relation type extension based on local and global views of relation instances. Locally, we extract features from the sentence that contains the instance. Globally, we measure the distributional similarity between instances from a 2 billion token corpus. Evaluation on the ACE 2004 corpus shows that LGCo-Testing can reduce annotation cost by 97% while maintaining the performance level of supervised learning.