Ranking and Feedback-based Stopping for Recall-Centric Document Retrieval
نویسندگان
چکیده
Systematic reviews require researchers to identify the entire body of relevant literature. Algorithms that filter the list for manual scanning with nearly perfect recall can significantly decrease the workload. This paper presents a novel stopping criterion that estimates the score-distribution of relevant articles from relevance feedback of random articles (S-D Minimal Sampling). Using 20 training and 30 test topics, we achieve a mean recall of 93.3%, filtering out 59.1% of the articles. This approach achieves higher F2-Scores at significantly reduced manual reviewing work loads. The method is especially suited for scenarios with sufficiently many relevant articles (>5) that can be sampled and employed for relevance feedback.
منابع مشابه
Document Image Retrieval Based on Keyword Spotting Using Relevance Feedback
Keyword Spotting is a well-known method in document image retrieval. In this method, Search in document images is based on query word image. In this Paper, an approach for document image retrieval based on keyword spotting has been proposed. In proposed method, a framework using relevance feedback is presented. Relevance feedback, an interactive and efficient method is used in this paper to imp...
متن کاملRRLUFF: Ranking function based on Reinforcement Learning using User Feedback and Web Document Features
Principal aim of a search engine is to provide the sorted results according to user’s requirements. To achieve this aim, it employs ranking methods to rank the web documents based on their significance and relevance to user query. The novelty of this paper is to provide user feedback-based ranking algorithm using reinforcement learning. The proposed algorithm is called RRLUFF, in which the rank...
متن کاملInvestigating the Impact of Authors’ Rank in Bibliographic Networks on Expertise Retrieval
Background and Aim: this research investigates the impact of authors’ rank in Bibliographic networks on document-centered model of Expertise Retrieval. Its purpose is to find out what kind of authors’ ranking in bibliographic networks can improve the performance of document-centered model. Methodology: Current research is an experimental one. To operationalize research goals, a new test colle...
متن کاملWeb pages ranking algorithm based on reinforcement learning and user feedback
The main challenge of a search engine is ranking web documents to provide the best response to a user`s query. Despite the huge number of the extracted results for user`s query, only a small number of the first results are examined by users; therefore, the insertion of the related results in the first ranks is of great importance. In this paper, a ranking algorithm based on the reinforcement le...
متن کاملEnhancing Relevance Models with Adaptive Passage Retrieval
Passage retrieval and pseudo relevance feedback/query expansion have been reported as two effective means for improving document retrieval in literature. Relevance models, while improving retrieval in most cases, hurts performance on some heterogeneous collections. Previous research has shown that combining passage-level evidence with pseudo relevance feedback brings added benefits. In this pap...
متن کامل