{"title":"结合句子级检索的信息检索模型","authors":"Jiali Zuo, Mingwen Wang, Jianyi Wan, Wenbing Luo","doi":"10.1109/IALP.2013.76","DOIUrl":null,"url":null,"abstract":"To get better performance, Some researchers have proposed relative work to exploit the position and proximity information of query terms in language model. However these models need large quantity of training data and its computation complexity is comparatively high. This paper presents an information retrieval model combining sentence level retrieval and use sentence as a unit to compute the relevant degree of the sentence to query. Experiment results show our model can get better performance than baseline models.","PeriodicalId":413833,"journal":{"name":"2013 International Conference on Asian Language Processing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Information Retrieval Model Combining Sentence Level Retrieval\",\"authors\":\"Jiali Zuo, Mingwen Wang, Jianyi Wan, Wenbing Luo\",\"doi\":\"10.1109/IALP.2013.76\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To get better performance, Some researchers have proposed relative work to exploit the position and proximity information of query terms in language model. However these models need large quantity of training data and its computation complexity is comparatively high. This paper presents an information retrieval model combining sentence level retrieval and use sentence as a unit to compute the relevant degree of the sentence to query. Experiment results show our model can get better performance than baseline models.\",\"PeriodicalId\":413833,\"journal\":{\"name\":\"2013 International Conference on Asian Language Processing\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Asian Language Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IALP.2013.76\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Asian Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IALP.2013.76","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Information Retrieval Model Combining Sentence Level Retrieval
To get better performance, Some researchers have proposed relative work to exploit the position and proximity information of query terms in language model. However these models need large quantity of training data and its computation complexity is comparatively high. This paper presents an information retrieval model combining sentence level retrieval and use sentence as a unit to compute the relevant degree of the sentence to query. Experiment results show our model can get better performance than baseline models.