Multidimensional Transformation-Based Learning
<|reference_start|>Multidimensional Transformation-Based Learning: This paper presents a novel method that allows a machine learning algorithm following the transformation-based learning paradigm \cite{brill95:tagging} to be applied to multiple classification tasks by training jointly and simultaneously on all fields. The motivation for constructing such a system stems from the observation that many tasks in natural language processing are naturally composed of multiple subtasks which need to be resolved simultaneously; also tasks usually learned in isolation can possibly benefit from being learned in a joint framework, as the signals for the extra tasks usually constitute inductive bias.   The proposed algorithm is evaluated in two experiments: in one, the system is used to jointly predict the part-of-speech and text chunks/baseNP chunks of an English corpus; and in the second it is used to learn the joint prediction of word segment boundaries and part-of-speech tagging for Chinese. The results show that the simultaneous learning of multiple tasks does achieve an improvement in each task upon training the same tasks sequentially. The part-of-speech tagging result of 96.63% is state-of-the-art for individual systems on the particular train/test split.<|reference_end|>
@article{florian2001multidimensional,
    title={Multidimensional Transformation-Based Learning},
    author={Radu Florian, Grace Ngai},
    journal={Proceedings of the 5th Computational Natural Language Learning
  Workshop (CoNNL-2001), pages 1-8, Toulouse, France},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0107021},
    primaryClass={cs.CL}
}