The intention gap between users and queries results in ambiguous and broad queries. To solve these problems, subtopic mining has been studied, which returns a ranked list of possible subtopics according to their relevance, popularity, and diversity. This paper proposes a novel method to mine subtopics using simple patterns and a hierarchical structure of subtopic candidates. First, relevant and various phrases are extracted as subtopic candidates using simple patterns based on noun phrases and alternative partial-queries. Second, a hierarchical structure of the subtopic candidates is constructed using sets of relevant documents from a web document collection. Finally, the subtopic candidates are ranked considering a balance between popularity and diversity using this structure. In experiments, our proposed methods outperformed the baselines and even an external resource based method at high-ranked subtopics, which shows that our methods can be effective and useful in various search scenarios like result diversification. 
Subtopic mining using simple patterns and hierarchical structure of subtopic candidates from web documents