Multidimensional Transformation-Based Learning
| dc.creator | Florian, Radu | |
| dc.creator | Ngai, Grace | |
| dc.date | 2001-07-17 | |
| dc.date.accessioned | 2026-07-07T03:17:21Z | |
| dc.date.available | 2026-07-07T03:17:21Z | |
| dc.description | 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. | |
| dc.description | 8 pages, 2 figures, presented at CONLL 2001 | |
| dc.identifier | https://arxiv.org/abs/cs/0107021 | |
| dc.identifier | http://arxiv.org/abs/cs/0107021 | |
| dc.identifier | Proceedings of the 5th Computational Natural Language Learning Workshop (CoNNL-2001), pages 1-8, Toulouse, France | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/30693 | |
| dc.subject | Computation and Language | |
| dc.subject | I.2.7 | |
| dc.title | Multidimensional Transformation-Based Learning | |
| dc.type | text |