We present a new language pair agnostic approach to inducing bilingual vector spaces from non-parallel data without any other resource in a bootstrapping fashion. The paper systematically introduces and describes all key elements of the bootstrapping procedure: (1) starting point or seed lexicon, (2) the confidence estimation and selection of new dimensions of the space, and (3) convergence. We...

By performing experiments on publicly available multi-class datasets we examine the effect of bootstrapping on the bias/variance behaviour of error-correcting output code ensembles. We present evidence to show that the general trend is for bootstrapping to reduce variance but to slightly increase bias error. This generally leads to an improvement in the lowest attainable ensemble error, however...

We address the task of improving the quality of lexicon bootstrapping, i.e., of expanding a semantic lexicon on a given corpus. A main problem of iterative bootstrapping techniques is the fact that lexicon quality degrades gradually as more and more false terms are added. We propose to exploit linguistic variation between languages to reduce this problem of semantic drift with a knowledge-lean ...