{"title":"基于多序列比对的多词表达式识别","authors":"Ru Li, Lijun Zhong, Jianyong Duan","doi":"10.1109/ALPIT.2008.71","DOIUrl":null,"url":null,"abstract":"For the Multiword Expression (MWE) recognition, the Multiple Sequence Alignment (MSA) is proposed on the motivation of gene recognition. Because textual sequence is similar to gene sequence in pattern analysis. This MSA technique is combined with error-driven rules, with the improved efficiency beyond the traditional methods.It provides a guarantee for the MWE recall. It uses the dynamic programming method to prevent candidates from combinational explosion, and provides a global solution for pattern extraction instead of sub-pattern redundancy. Consequently, it has accurate measures for flexible patterns. In experiment, some advanced statistical measures are performed for ranking candidates. In the comparison experiment, the MSA approach achieved better results.","PeriodicalId":169222,"journal":{"name":"2008 International Conference on Advanced Language Processing and Web Information Technology","volume":"377 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Multiword Expression Recognition Using Multiple Sequence Alignment\",\"authors\":\"Ru Li, Lijun Zhong, Jianyong Duan\",\"doi\":\"10.1109/ALPIT.2008.71\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For the Multiword Expression (MWE) recognition, the Multiple Sequence Alignment (MSA) is proposed on the motivation of gene recognition. Because textual sequence is similar to gene sequence in pattern analysis. This MSA technique is combined with error-driven rules, with the improved efficiency beyond the traditional methods.It provides a guarantee for the MWE recall. It uses the dynamic programming method to prevent candidates from combinational explosion, and provides a global solution for pattern extraction instead of sub-pattern redundancy. Consequently, it has accurate measures for flexible patterns. In experiment, some advanced statistical measures are performed for ranking candidates. In the comparison experiment, the MSA approach achieved better results.\",\"PeriodicalId\":169222,\"journal\":{\"name\":\"2008 International Conference on Advanced Language Processing and Web Information Technology\",\"volume\":\"377 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Conference on Advanced Language Processing and Web Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ALPIT.2008.71\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Advanced Language Processing and Web Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ALPIT.2008.71","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multiword Expression Recognition Using Multiple Sequence Alignment
For the Multiword Expression (MWE) recognition, the Multiple Sequence Alignment (MSA) is proposed on the motivation of gene recognition. Because textual sequence is similar to gene sequence in pattern analysis. This MSA technique is combined with error-driven rules, with the improved efficiency beyond the traditional methods.It provides a guarantee for the MWE recall. It uses the dynamic programming method to prevent candidates from combinational explosion, and provides a global solution for pattern extraction instead of sub-pattern redundancy. Consequently, it has accurate measures for flexible patterns. In experiment, some advanced statistical measures are performed for ranking candidates. In the comparison experiment, the MSA approach achieved better results.