Skip to Main Content (Press Enter)

Logo UNIPD
  • ×
  • Home
  • People
  • Outputs
  • Organizations
  • Third Mission
  • Expertise & Skills

UNIFIND
Logo UNIPD

|

UNIFIND

unipd.it
  • ×
  • Home
  • People
  • Outputs
  • Organizations
  • Third Mission
  • Expertise & Skills
  1. Outputs

A study on a mixed stopping strategy for total recall tasks

Conference Paper
Publication Date:
2019
abstract:
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 [4–6]. 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.
Iris type:
04.01 - Contributo in atti di convegno
Keywords:
Probabilistic Models; Random Sampling; Total recall
List of contributors:
Di Nunzio, G. M.
Authors of the University:
DI NUNZIO GIORGIO MARIA
Handle:
https://www.research.unipd.it/handle/11577/3334183
Full Text:
https://www.research.unipd.it//retrieve/handle/11577/3334183/355081/paper14.pdf
Book title:
CEUR Workshop Proceedings
Published in:
CEUR WORKSHOP PROCEEDINGS
Journal
CEUR WORKSHOP PROCEEDINGS
Series
  • Overview

Overview

URL

http://ceur-ws.org/Vol-2441/paper14.pdf
  • Use of cookies

Powered by VIVO | Designed by Cineca | 26.5.1.0