Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
ACM Transactions on Information Systems (TOIS)
Introduction to Information Retrieval
Introduction to Information Retrieval
Answering general time sensitive queries
Proceedings of the 17th ACM conference on Information and knowledge management
Supervised query modeling using wikipedia
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Incorporating query expansion and quality indicators in searching microblog posts
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
TEMPER: a temporal relevance feedback method
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Estimation methods for ranking recent information
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Category-based query modeling for entity search
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Temporal models for microblogs
Proceedings of the 21st ACM international conference on Information and knowledge management
Cognitive temporal document priors
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Combining recency and topic-dependent temporal variation for microblog search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
How fresh do you want your search results?
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Improving pseudo-relevance feedback via tweet selection
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Using temporal bursts for query modeling
Information Retrieval
| Hi-index | 0.00 |
We present an approach to query modeling that uses the temporal distribution of documents in an initially retrieved set of documents. Such distributions tend to exhibit bursts, especially in news-related document collections. We hypothesize that documents in those bursts are more likely to be relevant and update the query model with the most distinguishing terms in high-quality documents sampled from bursts. We evaluate the effectiveness of our models on a test collection of blog posts.