Improved Statistical Language Models

小行星9319516 这是一个为期三年的连续资助的第一年奖，用于研究在上下文无关解析应用中使用概率和概率语言模型。 本研究包括四个部分：1）通过对手写标记的语料库应用标准统计技术自动生成概率语法，将概率应用于英语句法分析; 2）统计分析中的一部分资源涉及词的共现，这提高了语言模型的有效性，从而有助于在解析中确定例如名词比另一个名词更可能处于某个位置，而不仅仅是通过经典解析器获得的词性结果; 3）由于统计参数被平滑，通过将具有相似统计的单词分组到类中，可以预期某种程度的语义概括是可能的;最后，4）通过诸如每个字的交叉熵的客观质量度量来严格地测试所获得和平滑的模型。 其目标是改进目前用于语音识别的现有三元模型，并致力于更好的语言理解系统。

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.