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Craig Saunders

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8 papers
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8

JMLR Journal 2011 Journal Article

Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation

  • Yizhao Ni
  • Craig Saunders
  • Sandor Szedmak
  • Mahesan Niranjan

We propose a distance phrase reordering model (DPR) for statistical machine translation (SMT), where the aim is to learn the grammatical rules and context dependent changes using a phrase reordering classification framework. We consider a variety of machine learning techniques, including state-of-the-art structured prediction methods. Techniques are compared and evaluated on a Chinese-English corpus, a language pair known for the high reordering characteristics which cannot be adequately captured with current models. In the reordering classification task, the method significantly outperforms the baseline against which it was tested, and further, when integrated as a component of the state-of-the-art machine translation system, MOSES, it achieves improvement in translation results. [abs] [ pdf ][ bib ] &copy JMLR 2011. ( edit, beta )

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JMLR Journal 2006 Journal Article

Kernel-Based Learning of Hierarchical Multilabel Classification Models

  • Juho Rousu
  • Craig Saunders
  • Sandor Szedmak
  • John Shawe-Taylor

We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is a variant of the Maximum Margin Markov Network framework, where the classification hierarchy is represented as a Markov tree equipped with an exponential family defined on the edges. We present an efficient optimization algorithm based on incremental conditional gradient ascent in single-example subspaces spanned by the marginal dual variables. The optimization is facilitated with a dynamic programming based algorithm that computes best update directions in the feasible set. Experiments show that the algorithm can feasibly optimize training sets of thousands of examples and classification hierarchies consisting of hundreds of nodes. Training of the full hierarchical model is as efficient as training independent SVM-light classifiers for each node. The algorithm's predictive accuracy was found to be competitive with other recently introduced hierarchical multi-category or multilabel classification learning algorithms. [abs] [ pdf ][ bib ] &copy JMLR 2006. ( edit, beta )

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NeurIPS Conference 2002 Conference Paper

String Kernels, Fisher Kernels and Finite State Automata

  • Craig Saunders
  • Alexei Vinokourov
  • John Shawe-Taylor

In this paper we show how the generation of documents can be thought of as a k-stage Markov process, which leads to a Fisher ker(cid: 173) nel from which the n-gram and string kernels can be re-constructed. The Fisher kernel view gives a more flexible insight into the string kernel and suggests how it can be parametrised in a way that re(cid: 173) flects the statistics of the training corpus. Furthermore, the prob(cid: 173) abilistic modelling approach suggests extending the Markov pro(cid: 173) cess to consider sub-sequences of varying length, rather than the standard fixed-length approach used in the string kernel. We give a procedure for determining which sub-sequences are informative features and hence generate a Finite State Machine model, which can again be used to obtain a Fisher kernel. By adjusting the parametrisation we can also influence the weighting received by the features. In this way we are able to obtain a logarithmic weighting in a Fisher kernel. Finally, experiments are reported comparing the different kernels using the standard Bag of Words kernel as a baseline.

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JMLR Journal 2002 Journal Article

Text Classification using String Kernels

  • Huma Lodhi
  • Craig Saunders
  • John Shawe-Taylor
  • Nello Cristianini
  • Chris Watkins

We propose a novel approach for categorizing text documents based on the use of a special kernel. The kernel is an inner product in the feature space generated by all subsequences of length k. A subsequence is any ordered sequence of k characters occurring in the text though not necessarily contiguously. The subsequences are weighted by an exponentially decaying factor of their full length in the text, hence emphasising those occurrences that are close to contiguous. A direct computation of this feature vector would involve a prohibitive amount of computation even for modest values of k, since the dimension of the feature space grows exponentially with k. The paper describes how despite this fact the inner product can be efficiently evaluated by a dynamic programming technique. Experimental comparisons of the performance of the kernel compared with a standard word feature space kernel (Joachims, 1998) show positive results on modestly sized datasets. The case of contiguous subsequences is also considered for comparison with the subsequences kernel with different decay factors. For larger documents and datasets the paper introduces an approximation technique that is shown to deliver good approximations efficiently for large datasets.

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