http://www.cnr.it/ontology/cnr/individuo/prodotto/ID272062

Optimizing text quantifiers for multivariate loss functions (Rapporti tecnici/preprint/working paper)

Type

Label

Optimizing text quantifiers for multivariate loss functions (Rapporti tecnici/preprint/working paper) (literal)

Anno

Alternative label

Esuli A., Sebastiani F. (2013)
Optimizing text quantifiers for multivariate loss functions
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Note

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#supporto

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

Titolo

Abstract

We address the problem of quantification, a supervised learning task whose goal is, given a class, to estimate the relative frequency (or prevalence) of the class in a dataset of unlabelled items. Quantification has several applications in IR, such as estimating the prevalence of positive reviews in a set of reviews of a given product, or estimating the prevalence of a given support issue in a dataset of transcripts of phone calls to tech support. So far, quantification has been addressed by learning a generic classifier, counting the unlabelled items which have been assigned the class, and tuning the obtained counts according to some heuristics. In this paper we depart from the tradition of using generic classifiers, and use instead a supervised learning model for structured prediction, capable of generating classifiers directly optimized for the (multivariate and non-linear) function used for evaluating quantification accuracy. Experiments on a very large, standard text classification dataset show that this method is more accurate, more stable, and more efficient than existing, state-of-the-art quantification methods. (literal)

Prodotto di

Autore CNR

Insieme di parole chiave

Prodotto

Autore CNR di

Insieme di parole chiave di

data.CNR.it