Index of papers in Proc. ACL 2011 that mention

**probability distribution**

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:

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:

- bigram (25)
- unigram (23)
- language models (17)

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:

- word pair (13)
- conditional probability (6)
- edit distance (5)