Abstract:
Traditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel
information about a topic, retrieval models need to capture dependencies
between documents. Such tasks require alternative evaluation and
optimization methods that operate on different types of relevance
judgements. We define faceted topic retrieval as a particular
novelty-driven task with the goal of finding a set of documents that
cover the different facets of an information need. A faceted topic
retrieval system must be able to cover as many facets as possible with
the smallest number of documents. We introduce two novel models for
faceted topic retrieval, one based on pruning a set of retrieved
documents, and one based on retrieving sets of documents through direct
optimization of evaluation measures. We compare the performance of our
models to Carbonell & Goldstein's Maximum Marginal Relevance (MMR) on a
set of 60 topics annotated with facets, showing that our models are
competitive.

We participated in the TREC - Web Track this year. Our performance and
our methodology would be discussed for the Diversity and Ad-hoc
Retrieval tasks. For more information on the TREC - Web Track visit
http://plg.uwaterloo.ca/~trecweb/
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