Paper: Modeling Category Structures With A Kernel Function

ACL ID W04-2408
Title Modeling Category Structures With A Kernel Function
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2004

We propose one type of TOP (Tangent vector Of the Posterior log-odds) kernel and apply it to text categorization. In a number of categoriza- tion tasks including text categorization, nega- tive examples are usually more common than positive examples and there may be several dif- ferent types of negative examples. Therefore, we construct a TOP kernel, regarding the prob- abilistic model of negative examples as a mix- ture of several component models respectively corresponding to given categories. Since each component model of our mixture model is ex- pressed using a one-dimensional Gaussian-type function, the proposed kernel has an advantage in computational time. We also show that the computational advantage is shared by a more general class of models. In our experiments, the proposed kern...