Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms
Collins, 2002, Discriminative Training Methods for Hidden Markov Models (Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002))
Subscriber deck — free during early access
1 cards in “6 Conclusions” · whole deck
6 Conclusions
- What makes the structured perceptron attractive compared with CRFs?It uses the same features and Viterbi decoding, but training needs only decoding and additive updates: no normalizers, no gradients of a partition function. Its guarantees depend on separability of the data rather than on the number of candidate outputs, and it beat a MaxEnt tagger on both tasks.
- Collins, 2002, Discriminative Training Methods for Hidden Markov Models (Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)), §6, p. 8
Source
- Michael Collins. 2002. Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), pp. 1–8. https://aclanthology.org/W02-1001/