Estimating query difficulty for news prediction retrieval
Pages 2623 - 2626
Abstract
News prediction retrieval has recently emerged as the task of retrieving predictions related to a given news story (or a query). Predictions are defined as sentences containing time references to future events. Such future-related information is crucially important for understanding the temporal development of news stories, as well as strategies planning and risk management. The aforementioned work has been shown to retrieve a significant number of relevant predictions. However, only a certain news topics achieve good retrieval effectiveness. In this paper, we study how to determine the difficulty in retrieving predictions for a given news story. More precisely, we address the query difficulty estimation problem for news prediction retrieval. We propose different entity-based predictors used for classifying queries into two classes, namely, Easy and Difficult. Our prediction model is based on a machine learning approach. Through experiments on real-world data, we show that our proposed approach can predict query difficulty with high accuracy.
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Index Terms
- Estimating query difficulty for news prediction retrieval
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Published In
October 2012
2840 pages
ISBN:9781450311564
DOI:10.1145/2396761
- General Chair:
- Xuewen Chen,
- Program Chairs:
- Guy Lebanon,
- Haixun Wang,
- Mohammed J. Zaki
Copyright © 2012 ACM.
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Published: 29 October 2012
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CIKM'12: 21st ACM International Conference on Information and Knowledge Management
October 29 - November 2, 2012
Hawaii, Maui, USA
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