Chinese semantic dependency analysis: Construction of a treebank and its use in classification

Semantic analysis is a standard tool in the Natural Language Processing (NLP) toolbox with widespread applications. In this article, we look at tagging part of the Penn Chinese Treebank with semantic dependency. Then we take this tagged data to train a maximum entropy classifier to label the semantic relations between headwords and dependents to perform semantic analysis on Chinese sentences. The classifier was able to achieve an accuracy of over 84%. We then analyze the errors in classification to determine the problems and possible solutions for this type of semantic analysis.

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