Chinese semantic dependency analysis: Construction of a treebank and its use in classification
Chinese semantic dependency analysis: Construction of a treebank and its use in classification
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DOI：
10.1145/1233912.1233914
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发表时间：
2007-05
期刊：
影响因子：
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通讯作者：
Jiajun Yan;D. Bracewell;S. Kuroiwa;F. Ren
中科院分区：
文献类型：
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作者：
Jiajun Yan;D. Bracewell;S. Kuroiwa;F. Ren
语义分析是自然语言处理（NLP）工具箱中的标准工具，具有广泛的应用。在这篇文章中，我们将研究使用语义依赖对Penn中文树库的一部分进行标注。然后，我们把这些标记的数据训练一个最大熵分类器标记的中心词和从属词之间的语义关系进行语义分析的中文句子。该分类器能够达到84%以上的准确率。然后，我们分析分类中的错误，以确定这种类型的语义分析的问题和可能的解决方案。
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.