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This study uses a novel simulation framework to evaluate whether the time and effort necessary to achieve high recall using active learning is reduced by presenting the reviewer with isolated sentences, as opposed to full documents, for relevance feedback. Under the weak assumption that more time and effort is required to review an entire document than a single sentence, simulation results indicate that the use of isolated sentences for relevance feedback can yield comparable accuracy and higher efficiency, relative to the state-of-the-art Baseline Model Implementation (BMI) of the AutoTAR Continuous Active Learning (CAL) method employed in the TREC 2015 and 2016 Total Recall Track.
We address the role of a user in Contextual Named Entity Retrieval (CNER), showing (1) that user identification of important context-bearing terms is superior to automated approaches, and (2) that further gains are possible if the user indicates the
Pseudo-relevance feedback mechanisms, from Rocchio to the relevance models, have shown the usefulness of expanding and reweighting the users initial queries using information occurring in an initial set of retrieved documents, known as the pseudo-rel
Dense retrieval systems conduct first-stage retrieval using embedded representations and simple similarity metrics to match a query to documents. Its effectiveness depends on encoded embeddings to capture the semantics of queries and documents, a cha
In this paper, we reflect on ways to improve the quality of bio-medical information retrieval by drawing implicit negative feedback from negated information in noisy natural language search queries. We begin by studying the extent to which negations
The ad-hoc retrieval task is to rank related documents given a query and a document collection. A series of deep learning based approaches have been proposed to solve such problem and gained lots of attention. However, we argue that they are inherent