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Subject:
From:
Giorgio Maria Di Nunzio <[log in to unmask]>
Reply To:
Giorgio Maria Di Nunzio <[log in to unmask]>
Date:
Thu, 16 Sep 2021 10:35:01 +0200
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[apologies for cross postings]

*** deadline extension October 30th 2021 ***

Final Call for Papers:

Special Issue on Technology-Assisted Review Systems

to be published in the Intelligent Systems with Applications (ISWA) Open Access Elsevier journal  

https://www.journals.elsevier.com/intelligent-systems-with-applications/


Important Dates:

Submission Deadline: 30th October 2021

Peer Review Due: 15th December 2021

Revision Due: 15th March 2022

Final Decision: 15th April 2022

Publication: May 2022

Dissemination, Composition and Review Procedures

 

Aims and Scopes: 

Given an information need, an exhaustive search aims to retrieve all the relevant information, if possible. There is a real demand for exhaustive search in domains like electronic discovery, systematic review, investigation, research, or even the construction of datasets for information retrieval evaluation. Technological support for this high-recall retrieval tasks uses machine learning to build (semi-) interactive systems that rank/classify documents more effectively compared to a traditional boolean keyword search.

This kind of systems, named Technology-Assisted Review (TAR) systems, tackle the problem by using classification or ranking algorithms to identify the relevant documents based on relevance feedback from expert reviewers, until a substantial number of the relevant documents have been identified.

The aim of these special issue is to investigate the most recent approaches of TAR systems applied on a wide variety of contexts where high recall is necessary.

 

The areas of interest of this special issue include, but are not limited to:

- Electronic discovery (e-Discovery): classification of correspondence, memos, emails, and balance sheets for documents that are relevant or responsive to a lawsuit or government investigation.

- Systematic reviews: reliably bring together findings from multiple studies that address a question and are often used to inform policy and practice, e.g., the development of medical guidelines in evidence-based medicine.

- Test collections: Assessing large-scale relevance labels is inevitable to build a high-quality test collection. Missing relevant labels in test collections causes bias when evaluating retrieval models.

This special issue will run as per the timeline given from submission to publication, while maintaining the rigorous peer review and high standards of the journal (https://www.journals.elsevier.com/intelligent-systems-with-applications/call-for-papers/technology-assisted-review-systems <https://www.journals.elsevier.com/intelligent-systems-with-applications/call-for-papers/technology-assisted-review-systems>).



All manuscripts submitted must be original, not under consideration elsewhere, and not previously published. A guide for authors and other relevant information for submission of manuscripts is available on the instructions for the author's page (https://www.elsevier.com/journals/intelligent-systems-with-applications/2667-3053/guide-for-authors <https://www.elsevier.com/journals/intelligent-systems-with-applications/2667-3053/guide-for-authors>). Authors can expect their manuscripts to be reviewed fairly, and in a skilled, conscientious manner. To enhance objectivity, and to guarantee high scientific quality and relevance to the subject, three peer reviewers will be selected to evaluate a manuscript. 

The peer review process shall be designed to avoid bias and conflict of interest on the part of reviewers and shall be composed of experts in the relevant field of research. A key criterion in publication decisions will be the manuscript’s fit for the special issue and the readership of ISWA. Papers will be published online as soon as accepted in a continuous flow. The final edition is scheduled for publication in May 2022.

 

Guest Editors:

- Giorgio Maria Di Nunzio, University of Padova, Italy (contact person [log in to unmask] <mailto:[log in to unmask]>)

- Evangelos Kanoulas, University of Amsterdam, The Netherlands
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