Machine learning in automated text categorisation (Articolo in rivista)

Type
Label
  • Machine learning in automated text categorisation (Articolo in rivista) (literal)
Anno
  • 2002-01-01T00:00:00+01:00 (literal)
Alternative label
  • Sebastiani F. 1 (2002)
    Machine learning in automated text categorisation
    in ACM computing surveys
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Sebastiani F. 1 (literal)
Pagina inizio
  • 1 (literal)
Pagina fine
  • 47 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • This paper has been recently determined by the Institute for Scientific Research (ISI -- http://www.isinet.com/) to be \"one of the most cited recent papers in the field of Computer Science\". See also the related interview with Fabrizio Sebastiani on the ISI Web site at http://www.esi-topics.com/fbp/2004/february04-FabrizioSebastiani.html. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 34 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • This paper has been recently determined by the Institute for Scientific Research (ISI -- http://www.isinet.com/) to be (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of reclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation. (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1 CNR-ISTI (literal)
Titolo
  • Machine learning in automated text categorisation (literal)
Abstract
  • The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of reclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation. (literal)
Prodotto di
Insieme di parole chiave

Incoming links:


Prodotto
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi
Insieme di parole chiave di
data.CNR.it