Improved Statistical Language Models

改进的统计语言模型

• 批准号: 9319516
• 负责人: Eugene Charniak
• 金额: $ 24.13万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-09-01 至 1998-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9319516&HistoricalAwards=false
• 关键词: Improved; Statistical; Language; Models

9319516 Charniak This is the first-year award of a three-year continuing grant to study the use of probabilities and probabilistic language models in context-free parsing applications. The research consists of four parts: 1) use of probabilities in the syntactic parsing of English through automatic generation of a probabilistic grammar by applying standard statistical techniques to corpora that has been hand- tagged; 2) a portion of the resources in the statistical analysis involves the co-occurrence of words, which improves the effectiveness of the language model thereby helping the determination in the parse that, for example, a noun would be more likely in a certain position than another, rather than just the parts-of-speech results that would be obtained via a classical parser; 3) it is anticipated that some level of semantic generalization may be possible by grouping the words with similar statistics in classes, as statistical parameters are smoothed; and finally, 4) the model obtained and smoothed is rigorously tested through an objective quality measure such as the per-word cross entropy. The goal is to improve upon existing trigram models, currently in use in speech recognition, and to work towards better language understanding systems.

9319516 Charniak这是为期三年的持续资助的第一年，用于研究概率和概率语言模型在上下文无关分析应用程序中的使用。这项研究包括四个部分：1)通过对手工标注的语料库应用标准统计技术，通过自动生成概率语法，在英语句法分析中使用概率；2)统计分析中的部分资源涉及单词的共现，这提高了语言模型的有效性，从而有助于在句法分析中确定，例如，一个名词在某个位置比在另一个位置更有可能，而不仅仅是通过经典句法分析器获得的词性结果；3)随着统计参数被平滑，可以通过将具有相似统计的词分组在类中来实现某种程度的语义泛化；以及最后，通过诸如每词交叉熵的客观质量度量来严格测试所获得和平滑的模型。目标是改进目前在语音识别中使用的现有三元模型，并努力实现更好的语言理解系统。