We represent words as sequences of substrings, and use the substrings as features in a Support Vector Machine (SVM) ranker, which is trained to rank possible stress patterns.

We use a support vector machine (SVM) to rank the possible patterns for each sequence (Section 3.2).

Table l: The steps in our stress prediction system (with orthographic and phonetic prediction examples): (1) word splitting, (2) support vector ranking of stress patterns, and (3) pattem-to-vowel

We adopt a Support Vector Machine (SVM) solution to these ranking constraints as described by J oachims (2002).

We divide each word into a sequence of substrings, and use these substrings as features for a Support Vector Machine (SVM) ranker.

Our method is based on recent advances in the field of statistical machine learning (multivariate capabilities of Support Vector Machines) and a rich feature space.

2.2 Support Vector Machines

Support Vector Machines (SVM) (Vapnik, 1995) are used to model classifiers S and L. SVM refers to a set of supervised learning algorithms that are based on margin maximization.