Shallow semantics for relation extraction

Abstract

This paper presents a new method for extracting meaningful relations from unstructured natural language sources. The method is based on information made available by shallow semantic parsers. Semantic information was used (1) to enhance a dependency tree kernel; and (2) to build semantic dependency structures used for enhanced relation extraction for several semantic classifiers. In our experiments the quality of the extracted relations surpassed the results of kernel-based models employing only semantic class information.