Confidence based learning of a two-model committee for sequence labeling

Author

Mancev, D. ; Todorovic, B.

Author_Institution

Dept. of Comput. Sci., Univ. of Nis, Nis, Serbia

fYear

2012

fDate

20-22 Sept. 2012

Firstpage

167

Lastpage

170

Abstract

The paper presents the use of a two structural model committee, where the output of the first model together with its confidence is set as the input of the second model. The confidence for the given context of predictions in the sequence is extracted from the alternative hypotheses generated from the first model. We present experiments on the shallow parsing, comparing the performance of the proposed method to the separate models.

Keywords

grammars; learning (artificial intelligence); confidence based learning; sequence labeling; shallow parsing; two structural model committee; Context; Hidden Markov models; Labeling; Machine learning; Predictive models; Support vector machines; Training; conditional random fields; confidence-based learning; sequence labeling; structural learning; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Network Applications in Electrical Engineering (NEUREL), 2012 11th Symposium on

Conference_Location

Belgrade

Print_ISBN

978-1-4673-1569-2

Type

conf

DOI

10.1109/NEUREL.2012.6419998

Filename

6419998

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3071561