Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning to Probabilistically Identify Authoritative Documents
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
Query expansion using random walk models
Proceedings of the 14th ACM international conference on Information and knowledge management
Metric Learning for Text Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph kernels between point clouds
Proceedings of the 25th international conference on Machine learning
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Improving biomedical document retrieval using domain knowledge
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Query similarity by projecting the query-flow graph
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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In this paper, we propose a new framework for constructing text metrics which can be used to compare and support inferences among terms and sets of terms. Our metric is derived from data-driven kernels on graphs that let us capture global relations among terms and sets of terms, regardless of their complexity and size. To compute the metric efficiently for any two subsets of terms, we develop an approximation technique that relies on the precompiled term-term similarities. To scale-up the approach to problems with huge number of terms, we develop and experiment with a solution that subsamples the term space. We demonstrate the benefits of the whole framework on two text inference tasks: prediction of terms in the article from its abstract and query expansion in information retrieval.