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Users often search for entities instead of documents, and in this setting, are willing to provide extra input, in addition to a series of query terms, such as category information and example entities. The authors propose a general probabilistic framework for entity search to evaluate and provide insights in the many ways of using these types of input for query modeling. They focus on the use of category information and show the advantage of a category-based representation over a term-based representation, and also demonstrate the effectiveness of category-based expansion using example entities. Their best performing model shows very competitive performance on the INEX-XER entity ranking and list completion tasks.
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