Chinese topic link detection based on semantic domain language model

Abstract

Topic link detection is a foundational research in the field of topic detection and tracking, which detects whether two random stories talk about the same topic. This paper proposes a method of applying semantic domain language model to link detection, based on the structure relation among contents and the semantic distribution in a story, and also verifies the influence of the strategy incorporating dependency parsing into semantic description. Evaluation on Chinese Corpus of TDT4 show that the semantic domain language model substantially improved the performance of current detection system, whose minimum DET cost is reduced by about 3 percent.