Probability and Natural Language Processing

概率与自然语言处理

• 批准号: 8911122
• 负责人: Eugene Charniak
• 金额: $ 25.33万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-09-01 至 1993-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8911122&HistoricalAwards=false
• 关键词: Probability; Natural; Language; Processing

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.

计算机理解自然语言的主要问题之一是始终存在的歧义。单词有多种含义，句子可以用几种不同的语法方式解析，名词短语可以指文本中几种可能的对象，等等。通常不可能做出正确的选择，因为其他选择在逻辑上是不可能的。相反，正确的选择只是比其他选择更合理或更可能。为了更好地解决这些问题和相关问题（比如根据人物的行动决定故事人物的计划），研究者将把概率论的应用整合到语言理解中。特别是，所提出的系统将使用来自句子和上下文的各种线索，建立概率分布的贝叶斯网络表示。正如本研究目前的工作一样，概率将用于指导处理，并最终完全剔除不太可能的替代方案。