Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Weakly supervised natural language learning without redundant views
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Discriminative language modeling with conditional random fields and the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Joint learning improves semantic role labeling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
An end-to-end discriminative approach to machine translation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
When is self-training effective for parsing?
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Lexicon acquisition for dialectal Arabic using transductive learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Competitive generative models with structure learning for NLP classification tasks
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
| Hi-index | 0.00 |
An interesting question surrounding semi-supervised learning for NLP is: should we use discriminative models or generative models? Despite the fact that generative models have been frequently employed in a semi-supervised setting since the early days of the statistical revolution in NLP, we advocate the use of discriminative models. The ability of discriminative models to handle complex, high-dimensional feature spaces and their strong theoretical guarantees have made them a very appealing alternative to their generative counterparts. Perhaps more importantly, discriminative models have been shown to offer competitive performance on a variety of sequential and structured learning tasks in NLP that are traditionally tackled via generative models, such as letter-to-phoneme conversion (Jiampojamarn et al., 2008), semantic role labeling (Toutanova et al., 2005), syntactic parsing (Taskar et al., 2004), language modeling (Roark et al., 2004), and machine translation (Liang et al., 2006). While generative models allow the seamless integration of prior knowledge, discriminative models seem to outperform generative models in a "no prior", agnostic learning setting. See Ng and Jordan (2002) and Toutanova (2006) for insightful comparisons of generative and discriminative models.