Abstract

This paper presents an application of nonlinear neural networks to topic spotting. Neural networks allow us to model higherorder interaction between document terms and to simultaneously predict multiple topics using shared hidden features. In the context of this model, we compare two approaches to dimensionality reduction in representation: one based on term selection and another based on Latent Semantic Indexing (LSI). Two different methods are proposed for improving LSI representations for the topic spotting task. We find that term selection and our modified LSI representations lead to similar topic spotting performance, and that this performance is equal to or better than other published results on the same corpus. 1 Introduction Topic spotting is the problem of identifying which of a set of predefined topics are present in a natural language document. More formally, given a set of n topics and a document, the task is to output for each topic the probability that the topic is prese...

Citations