Our classification framework , directly inspired by Blum and Chawla ( 2001 ) , integrates both perspectives , optimizing its labeling of speech segments based on both individual speech-segment classification scores and preferences for groups of speech segments to receive the same label .
learning from labeled and unlabeled data using graph mincuts
A Blum S Chawla
As discussed in Section 1 , however , the conversational nature of our data implies the existence of various relationships that can be exploited to improve cumulative classification accuracy for speech segments belonging to the same debate . Our classification framework , directly inspired by Blum and Chawla ( 2001 ) , integrates both perspectives , optimizing its labeling of speech segments based on both individual speech-segment classification scores and preferences for groups of speech segments to receive the same label . In this section , we discuss the specific classification framework that we adopt and the set of mechanisms that we propose for modeling specific types of relationships .
Our classification framework , directly inspired by @@CITATION , integrates both perspectives , optimizing its labeling of speech segments based on both individual speech-segment classification scores and preferences for groups of speech segments to receive the same label .