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Probabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
| Content Provider | Semantic Scholar |
|---|---|
| Author | Carterette, Ben Chandar, Praveen |
| Copyright Year | 2009 |
| 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 dierent types of relevance judgments. We dene faceted topic retrieval as a particular novelty-driven task with the goal of nding a set of documents that cover the dierent 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 MMR and the probabilistic model due to Zhai et al. on a set of 60 topics annotated with facets, showing that our models are competitive. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://ir.cis.udel.edu/~carteret/papers/cikm09.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
