人工智能-项目实践-信息检索-文献检索系统界面_根据关键字检索相应文献并显示中英文对照信息的引擎资源-CSDN文库

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SmartDocRetrievalWeb-master.zip （10个子文件）

SmartDocRetrievalWeb-master

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<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Title</title> <link rel="stylesheet" href="../css/frame/bootstrap.css"> <link rel="stylesheet" href="../css/list.css"> </head> <body> <ul> <li class="doc_list_card"> <h5 class="doc_title">Multi-class Document Classification Using Improved Word Embeddings</h5> <ul class="author_list"> <li class="author_item">Benedict A. Rabut,</li> <li class="author_item">Arnel C. Fajardo,</li> <li class="author_item">Ruji P. Medina</li> </ul> <div class="doc_profile">ICTIR '19: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval</div> <div class="doc_subtract">ABSTRACT\nTerm discrimination value is among the three basic heuristics exploited, directly or indirectly, in almost all ranking models for ad-hoc Information Retrieval (IR). Query term discrimination in monolingual IR is usually estimated based on document or collection frequency of terms. In the query translation approach for CLIR, the discrimination value of a query term needs to be estimated based on document or collection frequencies of its translations, which is more challenging. We show that the existing estimation models do not correctly estimate and adequately reflect the difference between the discrimination power of query terms, which hurts retrieval performance. We then propose a new model to estimate discrimination values of query terms for CLIR and empirically demonstrate its impact in improving the CLIR performance.</div> </li> <li class="doc_list_card"> <h5 class="doc_title">Term Discrimination Value for Cross-Language Information Retrieval</h5> <ul class="author_list"> <li class="author_item">Benedict A. Rabut,</li> <li class="author_item">Arnel C. Fajardo,</li> <li class="author_item">Ruji P. Medina</li> </ul> <div class="doc_profile">ICTIR '19: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval</div> <div class="doc_subtract">ABSTRACT\nTerm discrimination value is among the three basic heuristics exploited, directly or indirectly, in almost all ranking models for ad-hoc Information Retrieval (IR). Query term discrimination in monolingual IR is usually estimated based on document or collection frequency of terms. In the query translation approach for CLIR, the discrimination value of a query term needs to be estimated based on document or collection frequencies of its translations, which is more challenging. We show that the existing estimation models do not correctly estimate and adequately reflect the difference between the discrimination power of query terms, which hurts retrieval performance. We then propose a new model to estimate discrimination values of query terms for CLIR and empirically demonstrate its impact in improving the CLIR performance.</div> </li> <li class="doc_list_card"> <h5 class="doc_title">Information Diffusion in Social Networks: a recommendation model in the educational context</h5> <ul class="author_list"> <li class="author_item">Benedict A. Rabut,</li> <li class="author_item">Arnel C. Fajardo,</li> <li class="author_item">Ruji P. Medina</li> </ul> <div class="doc_profile">ICTIR '19: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval</div> <div class="doc_subtract">ABSTRACT\nTerm discrimination value is among the three basic heuristics exploited, directly or indirectly, in almost all ranking models for ad-hoc Information Retrieval (IR). Query term discrimination in monolingual IR is usually estimated based on document or collection frequency of terms. In the query translation approach for CLIR, the discrimination value of a query term needs to be estimated based on document or collection frequencies of its translations, which is more challenging. We show that the existing estimation models do not correctly estimate and adequately reflect the difference between the discrimination power of query terms, which hurts retrieval performance. We then propose a new model to estimate discrimination values of query terms for CLIR and empirically demonstrate its impact in improving the CLIR performance.</div> </li> </ul> <script type="text/javascript" src="../js/frame/jquery.min.js"></script> <script type="text/javascript" src="../js/frame/bootstrap.js"></script> </body> </html>

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