Discovering and Comparing Topic Hierarchies

Hierarchies have been used for organization, summarization, and access to information, yet a lingering issue is how best to construct them. In this paper, our goal is to automatically create domain specific hierarchies that can be used for browsing a document set and locating relevant documents. We examine methods of automatically generating hierarchies and evaluating them. To this end, we compare and contrast two methods of generating topic hierarchies from the text of documents: one, subsumption hierarchies, uses subsumption relations found within document sets, and the other, lexical hierarchies, utilizes frequently used words within phrases. Our evaluation shows that subsumption hierarchies divide documents into smaller groups, allowing one to find all relevant documents without looking at as many non-relevant documents. However, such hierarchies are more likely to contain no path to a relevant document.