Statistical machine learning for information retrieval
The purpose of this work is to introduce and experimentally validate a framework, based on statistical machine learning, for handling a broad range of problems in information retrieval (IR). 
Probably the most important single component of this framework is a parametric statistical model of word relatedness. A longstanding problem in IR has been to develop a mathematically principled model for document processing which acknowledges that one sequence of words may be closely related to another even if the pair have few (or no) words in common. Until now, the word-relatedness problem has typically been addressed with techniques like automatic query expansion [75], an often successful though ad hoc technique which artificially injects new, related words into a document for the purpose of ensuring that related documents have some lexical overlap. 
In the past few years have emerged a number of novel probabilistic approaches to information processing—including the language modeling approach to document ranking suggested first by Ponte and Croft [67], the non-extractive summarization work of Mittal and Witbrock [87], and the Hidden Markov Model-based ranking of Miller et al. [61]. This thesis advances that body of work by proposing a principled, general probabilistic framework which naturally accounts for word-relatedness issues, using techniques from statistical machine learning such as the Expectation-Maximization (EM) algorithm [24]. Applying this new framework to the problem of ranking documents by relevancy to a query, for instance, we discover a model that contains a version of the Ponte and Miller models as a special case, but surpasses these in its ability to recognize the relevance of a document to a query even when the two have minimal lexical overlap. (Abstract shortened by UMI.)