Sciweavers

Share
NAACL
2003

Semantic Language Models for Topic Detection and Tracking

8 years 10 months ago
Semantic Language Models for Topic Detection and Tracking
In this work, we present a new semantic language modeling approach to model news stories in the Topic Detection and Tracking (TDT) task. In the new approach, we build a unigram language model for each semantic class in a news story. We also cast the link detection subtask of TDT as a two-class classification problem in which the features of each sample consist of the generative log-likelihood ratios from each semantic class. We then compute a linear discriminant classifier using the perceptron learning algorithm on the training set. Results on the test set show a marginal improvement over the unigram performance, but are not very encouraging on the whole.
Ramesh Nallapati
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NAACL
Authors Ramesh Nallapati
Comments (0)
books