Personalized Concept Hierarchy Construction

A concept hierarchy is a set of concepts and relations between those concepts. Since ancient times, concept hierarchies have been used to organize and access information. In some situations, task-specific and user-specific concept hierarchies are necessary to allow an overview and easy access a large set of documents. For example, in regulatory reforms, rule-makers in government regulatory agencies must quickly identify and respond to issues raised in public comments. A concept hierarchy constructed for a set of public comments hierarchically organizes the comments and a user is able to easily “drill down” into documents that discuss a specific topic. Particularly, this dissertation addresses how to construct concept hierarchies from text collections automatically or with a-human-in-the-loop. The novel metric-based concept hierarchy construction framework transforms concept hierarchy construction into a multicriterion optimization problem. It incrementally clusters concepts based on minimum evolution of hierarchy structure, as well as optimization derived from the modeling of concept abstractness and concept coherence. Moreover, this dissertation represents the semantic distance between concepts as a wide range of features, each of which corresponds to a stateof-the-art concept hierarchy construction technique, such as lexico-syntactic pattern, contextual information, and co-occurrence. The use of multiple features allows a further study of the interaction between features and di↵erent types of semantic relations as well as the interaction between features and concepts at di↵erent abstraction levels. Besides the automatic framework for concept hierarchy construction, this dissertation also proposes an e↵ective human-guided concept hierarchy construction framework to address personalization by learning from periodic manual guidance and directing the learned models towards personal preferences. Through human-computer interactions, the human and the