Storing and retrieving word phrases
Information Processing and Management: an International Journal
Elements of information theory
Elements of information theory
Term dependence: truncating the Bahadur Lazarsfeld expansion
Information Processing and Management: an International Journal
Probabilistic dependence and logistic inference in information retrieval
Probabilistic dependence and logistic inference in information retrieval
Text windows and phrases differing by discipline, location in document, and syntactic structure
Information Processing and Management: an International Journal
Text retrieval and filtering: analytic models of performance
Text retrieval and filtering: analytic models of performance
On the law of Zipf-Mandelbrot for multi-word phrases
Journal of the American Society for Information Science
Natural language processing in support of decision-making: phrases and part-of-speech tagging
Information Processing and Management: an International Journal
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Exploring term dependences in probabilistic information retrieval model
Information Processing and Management: an International Journal
Probabilistic information retrieval model for a dependency structured indexing system
Information Processing and Management: an International Journal
Modeling hypermedia-based communication
Information Sciences: an International Journal
Modeling hypermedia-based communication
Information Sciences: an International Journal
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Binary lexical relations for text representation in information retrieval
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Throughput analysis for a high-performance FPGA-accelerated real-time search application
International Journal of Reconfigurable Computing - Special issue on High-Performance Reconfigurable Computing
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There are regularities in the statistical information provided by natural language terms about neighboring terms. We find that when phrase rank increases, moving from common to less common phrases, the value of the expected mutual information measure (EMIM) between the terms regularly decreases. Luhn's model suggests that midrange terms are the best index terms and relevance discriminators. We suggest reasons for this principle based on the empirical relationships shown here between the rank of terms within phrases and the average mutual information between terms, which we refer to as the Inverse Representation – EMIM principle. We also suggest an Inverse EMIM term weight for indexing or retrieval applications that is consistent with Luhn's distribution. An information theoretic interpretation of Zipf's Law is provided. Using the regularity noted here, we suggest that Zipf's Law is a consequence of the statistical dependencies that exist between terms, described here using information theoretic concepts.