Probabilistic models in information retrieval
The Computer Journal - Special issue on information retrieval
Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
An information retrieval model using the fuzzy proximity degree of term occurences
Proceedings of the 2005 ACM symposium on Applied computing
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Gravitation-based model for information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Text Representation: From Vector to Tensor
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Tensor space model for document analysis
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Proximity-based document representation for named entity retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Advertising keyword suggestion based on concept hierarchy
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Term proximity scoring for keyword-based retrieval systems
ECIR'03 Proceedings of the 25th European conference on IR research
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Abstract: There is an implied assumption for keywords in the traditional Information Retrieval (IR) models: keywords are parallel to each other. In fact, there are some relations between terms in quite a few queries, and in these queries, perhaps one term is subordinate to another term according to the inner meanings of information needs. This is ''Higher-order IR''(HIR) defined in this paper, and we call traditional IR ''first-order IR'' instead. Some research fields such as Public Opinion Analysis, Chain of Events Analysis and Trend Analysis which reflect the vague concept of HIR are all special form of HIR. Apparently, traditional IR models cannot deal with HIR directly. We need a new HIR model to represent and organize the documents, queries and relevance relationship between them. In this paper, we propose ''Tensor Field Model'' (TFM), and its perspectives are field theory in physics and multilinear algebra in maths. We construct the tensor representations of documents and queries in TFM, presenting some key concepts such as term field, tensor product of term array and term field constant. Empirical results show that TFM is appropriate for HIR theoretically and formally compared with traditional models which simplify the HIR problems as first-order IR problems to some extent.