Semantic frame-based natural language understanding for intelligent topic detection agent

Abstract

Detecting the topic of documents can help readers construct the background of the topic and facilitate document comprehension. In this paper, we proposed a semantic frame-based method for topic detection that simulates such process in human perception. We took advantage of multiple knowledge sources and identified discriminative patterns from documents through frame generation and matching mechanisms. Results demonstrated that our novel approach can effectively detect the topic of a document by exploiting the syntactic structures, semantic association, and the context within the text. Moreover, it also outperforms well-known topic detection methods.