Information extraction as a basis for high-precision text classification
ACM Transactions on Information Systems (TOIS)
Question-answering by predictive annotation
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Topic detection and tracking evaluation overview
Topic detection and tracking
Robustness of regularized linear classification methods in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A question answering system supported by information extraction
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Offline strategies for online question answering: answering questions before they are asked
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Robustness of adaptive filtering methods in a cross-benchmark evaluation
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Utility-based information distillation over temporally sequenced documents
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Query Disambiguation Based on Novelty and Similarity User's Feedback
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Query Disambiguation Based on Novelty and Similarity User's Feedback
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Learning to rank relevant and novel documents through user feedback
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Information Processing and Management: an International Journal
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Traditional adaptive filtering systems learn the user's interests in a rather simple way - words from relevant documents are favored in the query model, while words from irrelevant documents are down-weighted. This biases the query model towards specific words seen in the past, causing the system to favor documents containing relevant but redundant information over documents that use previously unseen words to denote new facts about the same news event. This paper proposes news ways of generalizing from relevance feedback by augmenting the traditional bag-of-words query model with named entity wildcards that are anchored in context. The use of wildcards allows generalization beyond specific words, while contextual restrictions limit the wildcard-matching to entities related to the user's query. We test our new approach in a nugget-level adaptive filtering system and evaluate it in terms of both relevance and novelty of the presented information. Our results indicate that higher recall is obtained when lexical terms are generalized using wildcards. However, such wildcards must be anchored to their context to maintain good precision. How the context of a wildcard is represented and matched against a given document also plays a crucial role in the performance of the retrieval system.