Index of papers in Proc. ACL 2011 that mention
  • probability distribution
Nederhof, Mark-Jan and Satta, Giorgio
Definitions
Intuitively, propemess ensures that where a pair of nonterminals in two synchronous strings can be rewritten, there is a probability distribution over the applicable rules.
Definitions
We say a PSCFG is consistent if pg defines a probability distribution over the translation, or formally:
Discussion
Prefix probabilities and right prefix probabilities for PSCFGs can be exploited to compute probability distributions for the next word or part-of-speech in left-to-right incremental translation of speech, or alternatively as a predictive tool in applications of interactive machine translation, of the kind described by Foster et al.
Effective PSCFG parsing
The translation and the associated probability distribution in the resulting grammar will be the same as those in the source grammar.
Effective PSCFG parsing
Again, in the resulting grammar the translation and the associated probability distribution will be the same as those in the source grammar.
Introduction
Prefix probabilities can be used to compute probability distributions for the next word or part-of-speech.
Introduction
Prefix probabilities and right prefix probabilities for PSCFGs can be exploited to compute probability distributions for the next word or part-of-speech in left-to-right incremental translation, essentially in the same way as described by Jelinek and Lafferty (1991) for probabilistic context-free grammars, as discussed later in this paper.
Prefix probabilities
The next step will be to transform Qprefix into a third grammar gl’mfix by eliminating epsilon rules and unit rules from the underlying SCFG, and preserving the probability distribution over pairs
probability distribution is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Schütze, Hinrich
Experimental Setup
Table 2: Key to probability distributions
Experimental Setup
Table 2 is a key to the probability distributions we use.
Introduction
Language models, probability distributions over strings of words, are fundamental to many applications in natural language processing.
probability distribution is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Wang, Ziqi and Xu, Gu and Li, Hang and Zhang, Ming
Abstract
The log linear model is defined as a conditional probability distribution of a corrected word and a rule set for the correction conditioned on the misspelled word.
Introduction
The log linear model is defined as a conditional probability distribution of a corrected word and a rule set for the correction given the misspelled word.
Model for Candidate Generation
We define the conditional probability distribution of we and R(wm, we) given mm as the following log linear model:
probability distribution is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: