A Cache-Based Natural Language Model for Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space for Discrete Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th 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
Foundations of statistical natural language processing
Foundations of statistical natural language processing
A hidden Markov model information retrieval system
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Text classification using string kernels
The Journal of Machine Learning Research
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised named-entity extraction from the web: an experimental study
Artificial Intelligence
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
The Locally Weighted Bag of Words Framework for Document Representation
The Journal of Machine Learning Research
Unsupervised query segmentation using generative language models and wikipedia
Proceedings of the 17th international conference on World Wide Web
Discovering key concepts in verbose queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A general optimization framework for smoothing language models on graph structures
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Statistical Language Models for Information Retrieval
Statistical Language Models for Information Retrieval
Structure preserving embedding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Named entity mining from click-through data using weakly supervised latent dirichlet allocation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A proximity language model for information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Feature selection by nonparametric Bayes error minimization
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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The classical Bag-of-Word (BOW) model represents a document as a histogram of word occurrence, losing the spatial information that is invaluable for many text analysis tasks. In this paper, we present the Language Pyramid (LaP) model, which casts a document as a probabilistic distribution over the joint semantic-spatial space and motivates a multi-scale 2D local smoothing framework for nonparametric text coding. LaP efficiently encodes both semantic and spatial contents of a document into a pyramid of matrices that are smoothed both semantically and spatially at a sequence of resolutions, providing a convenient multi-scale imagic view for natural language understanding. The LaP representation can be used in text analysis in a variety of ways, among which we investigate two instantiations in the current paper: (1) multi-scale text kernels for document categorization, and (2) multi-scale language models for ad hoc text retrieval. Experimental results illustrate that: for classification, LaP outperforms BOW by (up to) 4% on moderate-length texts (RCV1 text benchmark) and 15% on short texts (Yahoo! queries); and for retrieval, LaP gains 12% MAP improvement over uni-gram language models on the OHSUMED data set.