A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information 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
Sentence level information patterns for novelty detection
Sentence level information patterns for novelty detection
Novelty as a form of contextual re-ranking: efficient KLD models and mixture models
Proceedings of the second international symposium on Information interaction in context
A Comparison Study for Novelty Control Mechanisms Applied to Web News Stories
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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The novelty task consists of finding relevant and novel sentences in a ranking of documents given a query. In the literature, different techniques have been applied to address this problem. Nevertheless, little is known about Language Models for novelty detection and, especially, the effect of smoothing on the selection of novel sentences. Language Models can be used to study novelty and relevance in a principled way. These statistical models have been shown to perform well empirically in many Information Retrieval tasks. In this work we study formally the effects of smoothing on novelty detection. To this aim, we compare different techniques based on the Kullback-Leibler divergence and we analyze the sensitivity of retrieval performance to the smoothing parameters. The ability of Language Modeling estimation methods to handle quantitatively the uncertainty associated to the use of natural language is a powerful tool that can drive the future development of novelty-based mechanisms.