Exploring Deep Belief Network for Chinese Relation Extraction

Relation extraction is a fundamental task in information extraction that identifies the semantic relationships between two entities in the text. In this paper, a novel model based on Deep Belief Network (DBN) is first presented to detect and classify the relations among Chinese entities. The experiments conducted on the Automatic Content Extraction (ACE) 2004 dataset demonstrate that the proposed approach is effective in handling high dimensional feature space including character N-grams, entity types and the position information. It outperforms the state-of-the-art learning models such as SVM or BP neutral network.