Statistical parsing of messages
HLT '90 Proceedings of the workshop on Speech and Natural Language
Augmenting a hidden Markov model for phrase-dependent word tagging
HLT '89 Proceedings of the workshop on Speech and Natural Language
A chart re-estimation algorithm for a probabilistic recursive transition network
Computational Linguistics
Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
Computational Linguistics - Special issue on using large corpora: I
A reestimation algorithm for Probabilistic Recursive Transition Network
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
An algorithm for estimating the parameters of unrestricted hidden stochastic context-free grammars
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
New developments in parsing technology
| Hi-index | 0.00 |
The paper presents a new algorithm for estimating the parameters of a hidden stochastic context-free grammar. In contrast to the Inside/Outside (I/O) algorithm it does not require the grammar to be expressed in Chomsky normal form, and thus can operate directly on more natural representations of a grammar. The algorithm uses a trellis-based structure as opposed to the binary branching tree structure used by the I/O algorithm. The form of the trellis is an extension of that used by the Forward/Backward algorithm, and as a result the algorithm reduces to the latter for components that can be modeled as finite-state networks. In the same way that a hidden Markov model (HMM) is a stochastic analogue of a finite-state network, the representation used by the new algorithm is a stochastic analogue of a recursive transition network, in which a state may be simple or itself contain an underlying structure.