Text quantification

Fabrizio Sebastiani

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

Abstract

In a number of applications involving text classification in recent years it has been pointed out that the final goal is not determining which class (or classes) individual unlabeled documents belong to, but determining the prevalence (or “relative frequency”) of each class in the unlabeled data. The latter task is known as text quantification (or prevalence estimation, or class prior estimation). The goal of this course was to introduce the audience to the problem of quantification, techniques that have been proposed for solving it, metrics used to evaluate them, applications in fields such as information retrieval, machine learning, and data mining, and to the open problems in the area.

Original languageEnglish
Title of host publicationInformation Retrieval - 9th Russian Summer School, RuSSIR 2015, Revised Selected Papers
PublisherSpringer Verlag
Volume573
ISBN (Print)9783319417172
Publication statusPublished - 2016
Event9th Russian Summer School in Information Retrieval, RuSSIR 2015 - St. Petersburg, Russian Federation
Duration: 24 Aug 201528 Aug 2015

Publication series

NameCommunications in Computer and Information Science
Volume573
ISSN (Print)18650929

Other

Other9th Russian Summer School in Information Retrieval, RuSSIR 2015
CountryRussian Federation
CitySt. Petersburg
Period24/8/1528/8/15

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Keywords

  • Text classification
  • Text quantification

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Sebastiani, F. (2016). Text quantification. In Information Retrieval - 9th Russian Summer School, RuSSIR 2015, Revised Selected Papers (Vol. 573). (Communications in Computer and Information Science; Vol. 573). Springer Verlag.