Risk Minimization and Language Modeling in Text Retrieval Dissertation 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 general probabilistic framework, and facilitates the development of new principled approaches to text retrieval with the potential of going beyond the traditional notion of topical relevance. In this framework, queries and documents are modeled using statistical language models (i.e., probabilistic models of text), user preferences are modeled through loss functions,
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Risk minimization and language modeling in text retrieval dissertation abstract
Zhai, ChengXiang
Follow ACM SIGIR Forum
, Volume 36 (2)
Association for Computing Machinery – Sep 1, 2002
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