Add Semantic Role to Dependency Structure Language Model for Topic Detection and Tracking

In this paper, an idea of adding semantic role to the dependency structure language model is proposed. Firstly, the dependency structure language model for topic detection and tracking is presented. Then we introduce the method to determine the semantic role for the constituents of a sentence. Finally, we add the semantic role to the dependency structure language model Compare the verbs of the sentences in the stories with a list of verbs related with the verb of the topic. Then, annotate the verbs with semantic roles. This can enable us establish a relation between topics and semantic roles. So, only stories whose sentences containing the right semantic roles are selected. We propose using this semantic information as an extension of the dependency structure language model in order to reduce the number of stories retrieved by the system, and get a high precision in topic detection and tracking.