{"title":"使用文档先验改进QA检索","authors":"J. Mayfield, Paul McNamee","doi":"10.1145/1148170.1148313","DOIUrl":null,"url":null,"abstract":"We present a simple way to improve document retrieval for question answering systems. The method biases the retrieval system toward documents that contain words that have appeared in other documents containing answers to the same type of question. The method works with virtually any retrieval system, and exhibits a statistically significant performance improvement over a strong baseline.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"99 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving QA retrieval using document priors\",\"authors\":\"J. Mayfield, Paul McNamee\",\"doi\":\"10.1145/1148170.1148313\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a simple way to improve document retrieval for question answering systems. The method biases the retrieval system toward documents that contain words that have appeared in other documents containing answers to the same type of question. The method works with virtually any retrieval system, and exhibits a statistically significant performance improvement over a strong baseline.\",\"PeriodicalId\":433366,\"journal\":{\"name\":\"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval\",\"volume\":\"99 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1148170.1148313\",\"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 29th annual international ACM SIGIR conference on Research and development in information retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1148170.1148313","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present a simple way to improve document retrieval for question answering systems. The method biases the retrieval system toward documents that contain words that have appeared in other documents containing answers to the same type of question. The method works with virtually any retrieval system, and exhibits a statistically significant performance improvement over a strong baseline.
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