Garrick Sherman successfully defended his PhD dissertation, "Document Expansion and Language Model Re-estimation for Information Retrieval," on August 22.
His committee included Associate Professor Jana Diesner, chair and director of research; Professor J. Stephen Downie; Professor Ted Underwood; and Associate Professor Jaime Arguello of the University of North Carolina at Chapel Hill.
From the abstract: Document expansion is the process of augmenting the text of a document with text drawn from one or more other documents. The purpose of this expansion is to increase the size of the term sample from which document representations, such as language models, may be estimated. While document expansion has been shown to improve the effectiveness of ad-hoc document retrieval, our work differs from previous work in a variety of ways. We propose a consistent language modeling approach to document expansion of full length documents. We also explore the use of one or more external document collections as sources of data during the expansion process. Our proposed methods prove successful in improving retrieval effectiveness over baselines. We also acknowledge that existing document expansion work, including our own, has relied on intuitive assumptions about the mechanisms by which it achieves its effects. In this thesis, we quantify aspects of document language model change resulting from expansion . . . Recognizing the potential for further retrieval effectiveness improvement by means of selective application of our model, we investigate methods for automatically predicting whether or not to expand individual documents and, if so, which expansion collection may yield the optimal document representation. We find that, although the document expansion retrieval model has proven effective overall, accurate prediction concerning the expansion of a given document depends too heavily on predicting the document's relevance.