34 research outputs found
Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to Reviewers
The goal of a technology-assisted review is to achieve high recall with low
human effort. Continuous active learning algorithms have demonstrated good
performance in locating the majority of relevant documents in a collection,
however their performance is reaching a plateau when 80\%-90\% of them has been
found. Finding the last few relevant documents typically requires exhaustively
reviewing the collection. In this paper, we propose a novel method to identify
these last few, but significant, documents efficiently. Our method makes the
hypothesis that entities carry vital information in documents, and that
reviewers can answer questions about the presence or absence of an entity in
the missing relevance documents. Based on this we devise a sequential Bayesian
search method that selects the optimal sequence of questions to ask. The
experimental results show that our proposed method can greatly improve
performance requiring less reviewing effort.Comment: This paper is accepted by SIGIR 201
Inconsistent Responsiveness Determination in Document Review: Difference of Opinion or Human Error?
This Article analyzes the inconsistency between different document review efforts on the same document collection to determine whether that inconsistency is due primarily to ambiguity in applying the definition of responsiveness to particular documents, or due primarily to human error. By examining documents from the TREC 2009 Legal Track, the Authors show that inconsistent assessments regarding the same documents are due in large part to human error. Therefore, the quality of a review effort is not simply a matter of opinion; it is possible to show objectively that some reviews, and some review methods, are better than others
Technology-Assisted Review in E-Discovery Can Be More Effective and More Efficient Than Exhaustive Manual Review
E-discovery processes that use automated tools to prioritize and select documents for review are typically regarded as potential cost-savers – but inferior alternatives – to exhaustive manual review, in which a cadre of reviewers assesses every document for responsiveness to a production request, and for privilege. This Article offers evidence that such technology-assisted processes, while indeed more efficient, can also yield results superior to those of exhaustive manual review, as measured by recall and precision, as well as F1, a summary measure combining both recall and precision. The evidence derives from an analysis of data collected from the TREC 2009 Legal Track Interactive Task, and shows that, at TREC 2009, technology-assisted review processes enabled two participating teams to achieve results superior to those that could have been achieved through a manual review of the entire document collection by the official TREC assessors
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