Text Classification using String Kernels

By Huma Mahmood Lodhi, Craig John Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J. C. H. Watkins. 2002

1,476 citations recorded by the source corpus.

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Citations in the corpus

Deriving Neural Architectures from Sequence and Graph Kernels (2017, background): “For example, in a string kernel (Lodhi et al., 2002), S may refer to all possible subsequences while a graph kernel (Vish-wanathan et al., 2010) would deal with possible paths in the graph.”
Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language (2014, methodology): “In particular, K RISP uses the string kernel introduced by Lodhi et al. (2002) to classify substrings in an NL sentence.”
A Survey on Metric Learning for Feature Vectors and Structured Data (2013, methodology): “Problem (8) is reformulated as a min-max problem and solved using smooth optimization (Nesterov, 2005). Overall, the algorithm has a fast convergence rate but each iteration has an O(d3) complexity. The method performs well in practice while inducing a lower-dimensional projection space than full-ra”
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis (2007, background): “Recently, kernel functions for non-vectorial structured data such as strings, trees, and graphs have been proposed (see, e.g., Lodhi et al., 2002; Duffy and Collins, 2002; Kashima and Koyanagi, 2002; Kondor and Lafferty, 2002; Kashima et al., 2003; Gärtner et al., 2003; Gärtner, 2003).”
Learning to Find Pre-Images (2003, methodology): “one may wish to find a string which is the pre-image of a rep resentation using a string kernel [7, 8].”
From sample similarity to ensemble similarity: probabilistic distance measures in reproducing kernel Hilbert space (2006, background): “For example, alternative representations include strings [14], graphs [15], lattices [16], statistical manifolds [17], [18], [19], and so on [20], [21].”
Kernel-Based Learning of Hierarchical Multilabel Classification Models (2006, background): “With the exponential family, the problem translates to the problem of maximizing the minimum linear margin wTφ(xi ,yi)−wTφ(xi ,y) in the log-space.”
Least-squares two-sample test (2011, methodology): “Recently, a novel idea of using the universal RKHS (or the Gaussian widths) yielding the maximum MMD value has been introduced (Sriperumbudur et al., 2009).”
Predicting linear B-cell epitopes using string kernels (2008, unknown): “In addition, we introduce the subsequence kernel (SSK), which has been successfully used in text classification (Lodhi et al ., 2002), but has been under-explored in macromolecular sequence classification applications.”
A Context-Theoretic Framework for Compositionality in Distributional Semantics (2011, methodology): “This idea has been used successfully in text classification (Lodhi et al. 2002) and also formed the basis of the author’s entry to the second Recognising Textual Entailment Challenge (Clarke 2006).”
Learning to sportscast: a test of grounded language acquisition (2008, methodology): “KRISP and KRISPER KRISP (Kate & Mooney, 2006) uses SVMs with string kernels (Lodhi et al., 2002) to learn semantic parsers.”
Support vector machines (2008, unknown): “Consequently, a large body of research has studied kernel functions for structured data, such as for analysis of strings and sequences (e.g. Watkins, 2000; Lodhi et al., 2002; Sonnenburg et al., 2007a), hierarchical representations and trees (e.”