{"title":"基于最小编辑距离的文本匹配算法","authors":"Yu Zhao, Huixing Jiang, Xiaojie Wang","doi":"10.1109/NLPKE.2010.5587852","DOIUrl":null,"url":null,"abstract":"This paper proposes a measurement based on Minimum Edit Distance (MED) to the similarity between two sets of MultiWord Expressions (MWEs), which we use to calculate matching degree between two documents. We test the matching algorithm in the position searching system. Experiments show that the new measurement has higher performance than the cosine distance.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Minimum edit distance-based text matching algorithm\",\"authors\":\"Yu Zhao, Huixing Jiang, Xiaojie Wang\",\"doi\":\"10.1109/NLPKE.2010.5587852\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a measurement based on Minimum Edit Distance (MED) to the similarity between two sets of MultiWord Expressions (MWEs), which we use to calculate matching degree between two documents. We test the matching algorithm in the position searching system. Experiments show that the new measurement has higher performance than the cosine distance.\",\"PeriodicalId\":259975,\"journal\":{\"name\":\"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NLPKE.2010.5587852\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NLPKE.2010.5587852","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Minimum edit distance-based text matching algorithm
This paper proposes a measurement based on Minimum Edit Distance (MED) to the similarity between two sets of MultiWord Expressions (MWEs), which we use to calculate matching degree between two documents. We test the matching algorithm in the position searching system. Experiments show that the new measurement has higher performance than the cosine distance.
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