A key component extraction method based on HMM and dependency parsing

Jianchu Kang, Songsong Pang, Jian Dong, Bowen Du, Jian Huang
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Abstract

Increasing attention has been paid for POI (Point of Interest) data query for travel information service. The correct extraction of key components in question is crucial for improving the accuracy of query results. The paper proposes a key component extraction method based on HMM (Hidden Markov Model) and dependency parsing. Firstly, the sentence pattern classifier is established by HMM. And then, questions are classified by classifier. Finally, combination of sentence pattern's structure, the four key components are extracted by dependency parsing. The results show that the F1-Measure is 0.83, which well proves the effectiveness of the method.
一种基于HMM和依赖关系分析的关键部件提取方法
旅游信息服务中的兴趣点(POI)数据查询越来越受到人们的关注。正确提取所讨论的关键组件对于提高查询结果的准确性至关重要。提出了一种基于隐马尔可夫模型和依赖关系分析的关键部件提取方法。首先,利用隐马尔可夫模型建立句型分类器。然后,问题被分类器分类。最后,结合句型的结构,通过依赖句法分析提取出四个关键成分。结果表明,F1-Measure为0.83,充分证明了该方法的有效性。
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