Risk minimization and language modeling in text retrieval dissertation abstract

tion Abstract ChengXiang Zhai (Advisor: John Lafferty) Language Technologies Institute School of Computer Science Carnegie Mellon University With the dramatic increase in online information in recent years, text retrieval is becoming increasingly important. Although many different text retrieval approaches have been proposed and studied in the past decades, it is still a significant scientific challenge to develop principled retrieval approaches that also perform well empirically; so far, the theoretically well-motivated models have rarely led to good performance directly. It is also a great challenge in retrieval to develop models that may go beyond the traditional notion of topical relevance and capture more user factors, such as topical redundancy and sub-topic diversity. This thesis presents a new text retrieval framework based on Bayesian decision theory. The framework unifies several existing retrieval models, including the language modeling approach proposed recently, within one...

• Type: Journal
• Year: 2002
• Where: SIGIR
• Authors: ChengXiang Zhai
• Keywords: Information Technology, Language Models, Retrieval Performance, SIGIR 2002, Text Retrieval