Entity relation extraction to free text

A novel approach of the entity relation extraction is proposed by this paper, it is different from the previous approaches, and the syntactic knowledge extraction is specific section, which automatically extracts the characteristic words and patterns based on hierarchy bootstrapping machine learning. It advocates using a small amount of seed information and a large collection of easily-obtained unlabeled data. Hierarchy bootstrapping makes use of seed words and seed patterns to build a learning program, which extracts more characteristic words using scalar clusters. These characteristic words have semantic similarity with seed words. Then more extraction patterns could be learned automatically and added to the knowledge base, moreover, we also pay attention to semantic and pragmatic knowledge for entity relation extraction. Moreover, the evaluation way belongs to the MUC. According to our experimental results, we can find it is useful method.