SGER: Using Text Coherence and Verbal Valence in Long- Distance N-grams

SGER：在长距离 N 元语法中使用文本连贯性和语言效价

• 批准号: 9704046
• 负责人: Daniel Jurafsky
• 金额: $ 5万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-01-15 至 1997-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9704046&HistoricalAwards=false
• 关键词: SGER; Using; Text; Coherence; Verbal

*** Building better speech recognizers requires augmenting n-gram grammars with sophisticated yet probabilistic linguistic knowledge. This project is building probabilistic models of two important pieces of syntactic/semantic knowledge: verb-argument constraints and semantic text coherence.(1) Verbs place strong constraints on the syntax and semantics of their arguments. This project is computing probabilities for the different argument structures that can co-occur with different verbs, and using these probabilities to augment standard trigram language models.(2) Texts and discourses tend to be semantically coherent;in particular the words that occur in a text tend to be semantically related to each other. This project is applying a model of word meaning called Latent Semantic Analysis (LSA) to ASR LMs. In LSA, a word-similarity metric is defined by computing a large matrix of word co-occurrence probabilities, which are then smoothed via Singular Value Decomposition, resulting in a generalized measure of semantic word-similarity. Trigram models can then increase the probability that similar words will occur near each other. Building these two stochastic models of linguistic knowledge, besides possible application in speech recognition LMs, word-sense disambiguation, or parsing, also helps bridge the gap between the structural models used in linguistics and the statistical models of speech engineering.***

***构建更好的语音识别者需要增强具有复杂但概率的语言知识的N-Gram语法。 该项目正在建立两个重要的句法/语义知识的概率模型：动词 - 题词的约束和语义文本连贯性。（1）动词对他们的论证的语法和语义构成了强大的约束。 该项目是可以与不同动词共同发生的不同参数结构的计算概率，并使用这些概率来增强标准的Trigram语言模型。（2）文本和话语倾向于在语义上相干；在文本中发生的单词往往与彼此相关。 该项目将称为潜在语义分析（LSA）的单词含义模型应用于ASR LMS。 在LSA中，通过计算一个单词共发生概率的大矩阵来定义单词相似度度量，然后通过单数值分解来平滑，从而导致语义单词相似之处的普遍测量。 然后，Trigram模型可以增加彼此近乎相似单词的概率。 构建了语言知识的这两种随机模型，除了在语音识别LMS，单词态度的歧义或解析中可能应用，还有助于弥合语言学中使用的结构模型与语音工程的统计模型之间的差距。*** ***