SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The Extreme Future: The Top Trends That Will Reshape the World for the Next 5, 10, and 20 Years
The Extreme Future: The Top Trends That Will Reshape the World for the Next 5, 10, and 20 Years
A language modeling framework for expert finding
Information Processing and Management: an International Journal
The Combination and Evaluation of Query Performance Prediction Methods
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Supporting analysis of future-related information in news archives and the web
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Overview of the INEX 2008 Entity Ranking Track
Advances in Focused Retrieval
Topic Difficulty Prediction in Entity Ranking
Advances in Focused Retrieval
Effective pre-retrieval query performance prediction using similarity and variability evidence
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Using coherence-based measures to predict query difficulty
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Estimating the Query Difficulty for Information Retrieval
Estimating the Query Difficulty for Information Retrieval
Ranking related news predictions
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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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.