Authors:
Elie Azeraf
1
;
2
;
Emmanuel Monfrini
1
and
Wojciech Pieczynski
1
Affiliations:
1
SAMOVAR, CNRS, Telecom SudParis, Institut Polytechnique de Paris, Evry, France
;
2
Watson Department, IBM GBS, avenue de l’Europe, Bois-Colombes, France
Keyword(s):
Linear-chain CRF, Hidden Markov Chain, Bayesian Segmentation, Natural Language Processing.
Abstract:
Practitioners successfully use hidden Markov chains (HMCs) in different problems for about sixty years. HMCs belong to the family of generative models and they are often compared to discriminative models, like conditional random fields (CRFs). Authors usually consider CRFs as quite different from HMCs, and CRFs are often presented as interesting alternatives to HMCs. In some areas, like natural language processing (NLP), discriminative models have completely supplanted generative models. However, some recent results show that both families of models are not so different, and both of them can lead to identical processing power. In this paper, we compare the simple linear-chain CRFs to the basic HMCs. We show that HMCs are identical to CRFs in that for each CRF we explicitly construct an HMC having the same posterior distribution. Therefore, HMCs and linear-chain CRFs are not different but just differently parametrized models.