Automatic phrase indexing for document retrieval
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
A general language model for information retrieval (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
Biterm language models for document retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
The Geometry of Information Retrieval
The Geometry of Information Retrieval
Dependence language model for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Linear feature-based models for information retrieval
Information Retrieval
A comparison of statistical significance tests for information retrieval evaluation
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Investigation of partial query proximity in web search
Proceedings of the 17th international conference on World Wide Web
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
A proximity language model for information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Positional language models for information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
An improved markov random field model for supporting verbose queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Expected reciprocal rank for graded relevance
Proceedings of the 18th ACM conference on Information and knowledge management
Bayesian generalized probability calculus for density matrices
Machine Learning
What can quantum theory bring to information retrieval
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A novel re-ranking approach inspired by quantum measurement
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Parameterized concept weighting in verbose queries
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A quasi-synchronous dependence model for information retrieval
Proceedings of the 20th ACM international conference on Information and knowledge management
Using the quantum probability ranking principle to rank interdependent documents
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Modeling higher-order term dependencies in information retrieval using query hypergraphs
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Hi-index | 0.00 |
Traditional information retrieval (IR) models use bag-of-words as the basic representation and assume that some form of independence holds between terms. Representing term dependencies and defining a scoring function capable of integrating such additional evidence is theoretically and practically challenging. Recently, Quantum Theory (QT) has been proposed as a possible, more general framework for IR. However, only a limited number of investigations have been made and the potential of QT has not been fully explored and tested. We develop a new, generalized Language Modeling approach for IR by adopting the probabilistic framework of QT. In particular, quantum probability could account for both single and compound terms at once without having to extend the term space artificially as in previous studies. This naturally allows us to avoid the weight-normalization problem, which arises in the current practice by mixing scores from matching compound terms and from matching single terms. Our model is the first practical application of quantum probability to show significant improvements over a robust bag-of-words baseline and achieves better performance on a stronger non bag-of-words baseline.