Probability and Natural Language Processing

计算机理解自然界的主要问题之一是 语言是永远存在的歧义。 单词有多个 意义，句子可以在语法上解析几个不同的 名词短语可以指几个可能的对象， 文本等。通常情况下，不可能做出正确的 选择，因为其他人在逻辑上是不可能的。 而是 正确的选择只是更合理，或可能，比 他人 这位研究者将整合概率的使用 理论融入到语言理解中，以便更好地解决 这些和相关的问题（如计划识别决定一个 故事人物的计划基于人物的行动）。 特别是，拟议中的系统将利用各种线索， 从句子以及上下文，建立一个贝叶斯 概率分布的网络表示。 如在 目前的工作，这项研究的概率将被用来 引导加工，最终完全剪辑的可能性较小 替代品.

One of the major problems in computer understanding of natural language is the ever present ambiguity. Words have multiple meanings, sentences can be grammatically parsed several different ways, noun phrases can refer to several possible objects in the text, etc. Typically it is not possible to make the correct choice because the others are logically impossible. Rather the correct choice is simply more plausible, or probable, than the others. This investigator will integrate the use of probability theory into language comprehension in order to better solve these and related problems (like plan recognition deciding on a story character's plans on the basis of the character's actions). In particular, the proposed system will use the various clues from the sentence as well as the context, to build up a Bayes network representation of the probability distribution. As in current work by this research the probabilities will be used to guide processing and eventually to completely clip less likely alternatives.