Building a Chinese Semantic Resource Based on Feature Structure

Abstract

Building large-scale semantic resource is one of the major tasks in Language Information Processing. We propose Feature Structure Theory, and apply this theory in building a large-scale Chinese semantic resource based on Penn Chinese Treebank corpus. The feature structure theory aims at addressing annotation problems from special sentence patterns, flexible word order, and serial noun phrase, etc., which are universal in Chinese. Annotation based on feature structure theory describes more semantic information than traditional approaches, and achieves higher annotating efficiency and higher accuracy.