ICRCS at Intent2: Applying Rough Set and Semantic Relevance for Subtopic Mining

The target of the subtopic mining subtask of NTCIR-10 Intent-2 Task is to return a ranked list of subtopics. To this end, this paper proposes a method to apply the rough set theory for redundancy reduction in subtopic mined from webpages. Besides, semantic similarity is used for subtopic relevance measure in the re-ranking process, computed with semantic features extracted by NLP tools and semantic dictionary. By using the reduction concept of rough set, we first construct rough set based model (RSBM) for subtopic mining. Next, we combine the rough set theory and semantic relevance into a new model (RS&SRM). Evaluation results show the effectiveness of our approach compared with a baseline frequency term based model (FTBM). The best performance is achieved by RS&SRM, with I-rec of 0.4046, D-nDCG of 0.4413 and D#-nDCG of 0.4229 on the subtask of Chinese subtopic mining.