问答系统一直以来都是自然语言处理领域的研究热点之一,然而现有问答系统技术对复合事实型问句的处理效果并不完美。为了增强问答系统理解复合事实型问句的能力,该文提出了一种针对复合事实型问句的分解方法: 使用基于树核的支持向量机对问句的分解类别进行识别,进而使用基于依存句法分析的方法生成分解结果。实验结果显示,在我们所构建的高质量问句分解语料库中,我们的方法对问句分解类别进行了准确的识别,同时也可以较好地生成嵌套型问句的子问句。
Abstract
Question answering systems have been one of the hot research areas of natural language processing for a long time. To enhance the ability of analyzing complex factoid questions in question answering systems, we presented a novel method to decompose complex factoid questions: using a tree kernel based support vector machine to recognize decomposition categories of questions, and generating decomposition results with a dependency parsing based method. The evaluation shows that based on the high quality question decomposition corpus we had built, our method recognizes question decomposition categories with high performance and generated sub-question series with high quality, especially for the nested-typeones.
关键词
问句分解 /
复合事实型问句 /
问句理解 /
问答系统 /
自然语言处理
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Key words
question decomposition /
complex factoid question /
question analysis /
question answering system /
natural language processing
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参考文献
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脚注
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基金
国家自然科学基金(61472105)
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