Improved topic discrimination of broadcast news using a model of multiple simultaneous topics

This paper presents a new method of topic spotting that attempts to retrieve detailed multiple simultaneous topics from broadcast news stories, each of which has about four different topics out of several thousand different topics. A new topic model uses a simple HMM where each state of the HMM represents one topic and the topic state emits topic-dependent keywords probabilistically. The model allows (unobserved) transitions among topics, word by word. These characteristics improve the discriminative ability between keywords and general words in a topic model and decrease the probabilistic overlap among the topic models more than the conventional topic models (such as a simple multinomial probability model). In addition, the model is not confused by words from multiple topics within one story. We applied the new method to topic spotting from manually transcribed texts of news shows. The new method showed better results in precision and recall rates than the conventional method.