@inproceedings{5070a3fe476f4653b72c7ee5f9c3e05a,
title = "Query performance prediction for entity retrieval",
abstract = "We address the query-performance-prediction task for entity retrieval; that is, retrieval effectiveness is estimated with no relevance judgements. First we show how to adapt state-of-the-art query-performance predictors proposed for document retrieval to the entity retrieval domain. We then present a novel predictor that is based on the cluster hypothesis. Evaluation performed with the INEX entity ranking track collections shows that our predictor can often out-perform the most effective predictors we experimented with.",
keywords = "Entity retrieval, Query performance prediction",
author = "Hadas Raviv and Oren Kurland and David Carmel",
year = "2014",
doi = "10.1145/2600428.2609519",
language = "English (US)",
isbn = "9781450322591",
series = "SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery",
pages = "1099--1102",
booktitle = "SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval",
note = "37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014 ; Conference date: 06-07-2014 Through 11-07-2014",
}