Jianchu Kang, Songsong Pang, Jian Dong, Bowen Du, Jian Huang
{"title":"一种基于HMM和依赖关系分析的关键部件提取方法","authors":"Jianchu Kang, Songsong Pang, Jian Dong, Bowen Du, Jian Huang","doi":"10.1109/ICAICT.2012.6398516","DOIUrl":null,"url":null,"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.","PeriodicalId":221511,"journal":{"name":"2012 6th International Conference on Application of Information and Communication Technologies (AICT)","volume":"412 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A key component extraction method based on HMM and dependency parsing\",\"authors\":\"Jianchu Kang, Songsong Pang, Jian Dong, Bowen Du, Jian Huang\",\"doi\":\"10.1109/ICAICT.2012.6398516\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":221511,\"journal\":{\"name\":\"2012 6th International Conference on Application of Information and Communication Technologies (AICT)\",\"volume\":\"412 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 6th International Conference on Application of Information and Communication Technologies (AICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAICT.2012.6398516\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 6th International Conference on Application of Information and Communication Technologies (AICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAICT.2012.6398516","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A key component extraction method based on HMM and dependency parsing
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.