Experiments in topic indexing of broadcast news using neural networks
Christoph Neukirchen, Daniel Willett, Gerhard Rigoll · 1999
The paper deals with the problem of extracting topic information from news show stories by statistical methods. It is shown that the traditional topic-dependent n-gram language modeling approach can be decomposed in order to apply neural networks for topic indexing. Two specific problems in training of these networks are addressed: a very sparse data distribution in the stories and the superposition of different topics in a story. The first problem is tackled by an integrated smoothing approach in the backpropagation method; an expansion of the neural network structure can be used to cope with topic mixtures in stories. Due to the efficient parameter sharing the application of neural networks results in a small improvement in topic indexing performance on a small corpus of broadcast news compared to the traditional topic-dependent n-gram method.