Using temporal profiles of queries for precision prediction
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Q2C@UST: our winning solution to query classification in KDDCUP 2005
ACM SIGKDD Explorations Newsletter
ACM Transactions on Information Systems (TOIS)
Searching blogs and news: a study on popular queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Scalable and near real-time burst detection from eCommerce queries
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving search relevance for implicitly temporal queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Search result re-ranking by feedback control adjustment for time-sensitive query
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Time is of the essence: improving recency ranking using Twitter data
Proceedings of the 19th international conference on World wide web
Context comparison of bursty events in web search and online media
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Learning recurrent event queries for web search
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Query-Guided Event Detection From News and Blog Streams
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Twevent: segment-based event detection from tweets
Proceedings of the 21st ACM international conference on Information and knowledge management
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Many but not all popular queries are related to ongoing or recent events. In this paper, we identify 20 features including both contextual and temporal features from a small set of search results of a query and predict its event-relatedness. Search results from news and blog search engines are evaluated. Our analysis shows that the number of named entities in search results and their appearances in Wikipedia are among the most discriminative features for query event-relatedness prediction. Our study also shows that contextual features are more effective than temporal features. Evaluated with four classifiers (i.e., Support Vector Machine, Naive Bayes, Multinomial Logistic Regression, and Bayesian Logistic Regression) on two datasets, our experiments show that query event-relatedness can be predicted with high accuracy using the proposed features.