Overview of the second text retrieval conference (TREC-2)
TREC-2 Proceedings of the second conference on Text retrieval conference
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Retrieval and novelty detection at the sentence level
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
Redundant documents and search effectiveness
Proceedings of the 14th ACM international conference on Information and knowledge management
Novelty detection based on sentence level patterns
Proceedings of the 14th ACM international conference on Information and knowledge management
Sentence level information patterns for novelty detection
Sentence level information patterns for novelty detection
Novelty detection using local context analysis
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
The effect of smoothing in language models for novelty detection
FDIA'07 Proceedings of the 1st BCS IRSG conference on Future Directions in Information Access
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Current Information Retrieval systems are often based on topicality. They estimate relevance by comparing the similarity between the user query and each document. These systems do not take into account important contextual information. More specifically, they do not often apply mechanisms to filter out redundant information. We interpret context here as the set of chunks of text from the ranked set of documents that the user has already seen. This is a valuable contextual information to guide the retrieval processes in a way that avoids redundancy. It is desirable that the ranking of results is composed by relevant but also novel material. This means that each document must provide to the user unseen information which is related to his need. In this work we study different novelty detection approaches that make good use of this contextual information. We show that these techniques can be applied effectively and efficiently at the sentence level.