Text quantification

Fabrizio Sebastiani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In recent years it has been pointed out that, in a number of applications involving classification, the final goal is not determining which class (or classes) individual unlabelled data items belong to, but determining the prevalence (or "relative frequency") of each class in the unlabelled data. The latter task has come to be known as quantification [1, 3, 5-10, 15, 18, 19].

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages819-822
Number of pages4
Volume8416 LNCS
ISBN (Print)9783319060279
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event36th European Conference on Information Retrieval, ECIR 2014 - Amsterdam
Duration: 13 Apr 201416 Apr 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8416 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other36th European Conference on Information Retrieval, ECIR 2014
CityAmsterdam
Period13/4/1416/4/14

    Fingerprint

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Sebastiani, F. (2014). Text quantification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8416 LNCS, pp. 819-822). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8416 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-06028-6_104