A study on a mixed stopping strategy for total recall tasks

How do we calculate how many relevant documents are in a collection? In this abstract, we discuss our line of research about total recall systems such as interactive system for systematic reviews based on an active learning framework [4\u20136]. In particular, we will present 1) the problem in mathematical terms, and 2) the experiments of an interactive system that continuously monitors the costs of reviewing additional documents and suggests the user whether to continue or not in the search based on the available remaining resources. We will discuss the results of this system on the ongoing CLEF 2019 eHealth task

Total Recall, Language Processing, and Software Engineering

A broad class of software engineering problems can be generalized as the "total recall problem". This short paper claims that identifying and exploring total recall language processing problems in software engineering is an important task with wide applicability. To make that case, we show that by applying and adapting the state of the art active learning and text mining, solutions of the total recall problem, can help solve two important software engineering tasks: (a) supporting large literature reviews and (b) identifying software security vulnerabilities. Furthermore, we conjecture that (c) test case prioritization and (d) static warning identification can also be categorized as the total recall problem. The widespread applicability of "total recall" to software engineering suggests that there exists some underlying framework that encompasses not just natural language processing, but a wide range of important software engineering tasks.Comment: 4 pages, 2 figures. Submitted to NL4SE@ESEC/FSE 201

Streamlined Data Fusion: Unleashing the Power of Linear Combination with Minimal Relevance Judgments

Linear combination is a potent data fusion method in information retrieval tasks, thanks to its ability to adjust weights for diverse scenarios. However, achieving optimal weight training has traditionally required manual relevance judgments on a large percentage of documents, a labor-intensive and expensive process. In this study, we investigate the feasibility of obtaining near-optimal weights using a mere 20\%-50\% of relevant documents. Through experiments on four TREC datasets, we find that weights trained with multiple linear regression using this reduced set closely rival those obtained with TREC's official "qrels." Our findings unlock the potential for more efficient and affordable data fusion, empowering researchers and practitioners to reap its full benefits with significantly less effort.Comment: 12 pages, 8 figure

CLEF 2017 technologically assisted reviews in empirical medicine overview

Systematic reviews are a widely used method to provide an overview over the current scientific consensus, by bringing together multiple studies in a reliable, transparent way. The large and growing number of published studies, and their increasing rate of publication, makes the task of identifying all relevant studies in an unbiased way both complex and time consuming to the extent that jeopardizes the validity of their findings and the ability to inform policy and practice in a timely manner. The CLEF 2017 e-Health Lab Task 2 focuses on the efficient and effective ranking of studies during the abstract and title screening phase of conducting Diagnostic Test Accuracy systematic reviews. We constructed a benchmark collection of fifty such reviews and the corresponding relevant and irrelevant articles found by the original Boolean query. Fourteen teams participated in the task, submitting 68 automatic and semi-automatic runs, using information retrieval and machine learning algorithms over a variety of text representations, in a batch and iterative manner. This paper reports both the methodology used to construct the benchmark collection, and the results of the evaluation