Multitext Fusion, Tracking and Trend Detection

多文本融合、跟踪和趋势检测

• 批准号: 9314992
• 负责人: Jaime Carbonell
• 金额: $ 75万
• 依托单位: Carnegie-Mellon 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: 1994-03-15 至 1998-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9314992&HistoricalAwards=false
• 关键词: Multitext; Fusion; Tracking; Trend; Detection

9314959 Cole This is the first year of a continuing award in the NSF/ARPA Human Language Technology initiative involving cooperation between the Oregon Graduate Institute and the International Computer Science Institute. The proposed project considers research on: a) signal representation methodologies that would combat environmental noise and non-linearities which detract from automatic speech recognition, b) speaker variability which is a source of uncertainty also in recognition, and is to be tackled from a probabilistic point of view from collected (telephone) speech corpus, and, c) dialogue enhancement methodologies and strategies, including recovery, in order to increase the probability of correct recognition. Each of these three areas comprise major deterrents to robust recognition that will be investigated. The overall objective of the project is to consider integrated approaches to spoken-language system robustness. 9314946 Flanagan This is the first year of a continuing award in the NSF/ARPA Human Language Technology for cooperation between researchers at Rutgers University's Center for Computer Aids for Industrial Productivity (CAIP), AT&T Bell Laboratories and General Dynamics Electric Boat Division. The research to be conducted concerns the generation of speech signals in terms of (a) an articulatory description of the vocal system, and (b) a fluid dynamic solution to the generation, propagation, and radiation of audible sound produced by the acoustic system. This includes the computation of the speech signal from first principles, using the Navier- Stokes description of fluid flow, already demonstrated feasible. Anticipated results include a potentially significant improvement in the quality of synthesized speech and fundamentally new and more robust designs of speech recognizers stemming from a better understanding of the speech phenomena and how it can be made more immune to interference. Also it is expected that t his research influence improvements in the coding of speech at lower bit rates. 9314967 Price This is the first year of a continuing award in the NSF/ARPA Human Language Technology initiative involving cooperation between researchers at SRI International, Stanford University and the Massachusetts Institute of Technology. The proposed project considers research on the hypothesis that disfluencies in spontaneous speech - pauses, repeated words, repairs, filled pauses, word fragments, and elongated segments - are far from random and that knowledge about their regularity would shed light on aspects of human cognition and provide principled methods for dealing with them in spontaneous speech processing. Several relevant disciplines are involved in this effort such as human- computer interaction, linguistics, psycho-linguistics, computational linguistics, prosody, and speech technology. The multi-disciplinary approach includes investigating the forms and distribution of disfluencies across many corpora, conducting perceptual experiments to assess the saliency of specific cues in the signal, and developing and evaluating new methods for automatic processing of speech. 9314955 Pustejovsky This is the first year of a continuing award in the NSF/ARPA Human Language Technology initiative involving cooperation between researchers at Brandeis University and at Apple Computer. The research involves the building of a generative lexical engine at the core which aids in the determination of word sense following lexical properties considered composable in a given phrasal context. The project instantiates a lexical semantic segment of a substantial fragment of English by constructing such core lexical engine with the following components: a semantic typing system, relational structures for all categories, and generative mechanisms enabling extension and identification of word sense in context. The project is complementary to other initiatives to develop linguistic infrastructure resources on a large scale, such as COMLEX and WordNet. The project develops mechanisms that carry out the specialized lexical inferences that result, through composition of lexical types, in word sense determination. 9314969 Sleator This is the first year of a continuing award in the NSF/ARPA Human Language Technology initiative involving cooperation between researchers from Carnegie Mellon University and International Business Machines. This project intends takes advantage of the simplicity of the classical statistical trigram model of language while augmenting it with the syntactic and semantic aspects which constrain the use of the new grammatical trigram model to advantage over the purely stochastic model. The concepts of probabilistic link grammars are used in this research, incorporating trigrams into a unified framework for modeling long-distance grammatical dependencies in computationally efficient ways. The methods proposed are expected to have greater predictive power over current methods from the point of view of entropy measurements, and to integrate finite-state automata models and new statistical estimation algorithms with modern powerful machines resulting in improved speech recognition, translation, and understanding systems. 9314961 Thomason This is the first year of a continuing award in the NSF/ARPA Human Language Technology initiative involving cooperation between the University of Pittsburgh and the Stanford Research Institute. This project involves research on integrating the intentional and informational perspectives in architectures for interpretation and generation of interactive discourse. Particular problems investigated include the recognition by the listener of the speaker s plan, a formalization of the notion of conversational record, discourse structure from a computational point of view, and analysis of implicatures involving quantity and similar phenomena involvin g interactions between the processes of generation and interpretation. Bases for the research are the use of abductive inference in finite-state approximation methods and in knowledge-based systems, accommodation processes in interactive discourse, generation of coherent text, use of defeasible reasoning in plan recognition, and utterance planning to achieve communicative goals. 9314992 Young This is the first year of a continuing award in the NSF/ARPA Human Language Technology initiative involving cooperation between Carnegie Mellon University and the SRA Corporation. This project involves the investigation of methodologies for the extraction of information from text and its summarization in structured data records for subsequent automatic processing. The approach includes merging, fusing and consolidating multiple texts that address the same topic at one or more points in time and use of the results in the augmentation of existing knowledge bases, as well as in the detection of potential trends in the data. The basic issues investigated involve metrics to assess information consistency and redundancy, the probabilistic unification of multiple co-referential texts, methods for unifying representations of texts describing events that evolve over time, and constrained structural induction for predicting trends. The project uses pre-extracted representations of texts from the ARPA TIPSTER project, known to be noisy by containing errors of omission and commission, so as to increase the tractability of the project in an information rich description of events at single or evolving points in time, and in order to develop robust methods in the presence of inconsistencies and partial information.

这是NSF/ARPA人类语言技术计划持续奖励的第一年，涉及俄勒冈研究生院和国际计算机科学研究所之间的合作。建议项目考虑研究以下内容：A)对抗环境噪声和非线性的信号表示方法，这些噪声和非线性会影响自动语音识别；b)说话人的可变性也是识别中的不确定性来源，需要从收集的（电话）语音语料库的概率角度来解决；c)对话增强方法和策略，包括恢复，以增加正确识别的概率。这三个领域中的每一个都构成了将被调查的强大识别的主要障碍。该项目的总体目标是考虑口语系统鲁棒性的综合方法。今年是美国国家科学基金会/美国国防部高级研究计划署人类语言技术奖的第一年，该奖是为了奖励罗格斯大学工业生产力计算机辅助中心（CAIP）、at&t贝尔实验室和通用动力电船部门的研究人员之间的合作。将要进行的研究涉及语音信号的产生，包括(a)发声系统的发音描述，以及(b)声学系统产生的可听声音的产生、传播和辐射的流体动力学解决方案。这包括从第一性原理计算语音信号，使用流体流动的Navier- Stokes描述，已经证明是可行的。预期的结果包括合成语音质量的潜在显著改善，以及基于对语音现象的更好理解以及如何使其更不受干扰而产生的新的、更健壮的语音识别器设计。同时，本研究也有望对低比特率语音编码的改进产生影响。今年是NSF/ARPA人类语言技术计划持续奖励的第一年，该计划涉及SRI国际、斯坦福大学和麻省理工学院的研究人员之间的合作。提议的项目考虑了以下假设的研究，即自发语音中的不流畅——停顿、重复单词、修复、填充停顿、单词片段和拉长的片段——远非随机的，并且关于它们的规律性的知识将揭示人类认知的各个方面，并为在自发语音处理中处理它们提供原则性的方法。几个相关的学科涉及到这方面的努力，如人机交互、语言学、心理语言学、计算语言学、韵律学和语音技术。多学科的方法包括研究语料库中不流畅的形式和分布，进行感知实验来评估信号中特定线索的显著性，以及开发和评估语音自动处理的新方法。这是NSF/ARPA人类语言技术计划持续奖励的第一年，该计划涉及布兰代斯大学和苹果计算机公司的研究人员之间的合作。该研究的核心是构建一个生成式词汇引擎，该引擎有助于根据给定短语上下文中被认为可组合的词汇属性确定词义。该项目通过构建这样的核心词汇引擎，实例化了一个大量英语片段的词汇语义段，该引擎包含以下组件：语义类型系统，所有类别的关系结构，以及能够在上下文中扩展和识别词义的生成机制。该项目是对其他大规模开发语言基础设施资源（如complex和WordNet）的补充。该项目开发的机制，执行专门的词汇推理，结果，通过词汇类型的组成，在词义确定。今年是美国国家科学基金会/美国国防部高级研究计划局人类语言技术计划的第一年，该计划涉及卡内基梅隆大学和国际商业机器公司的研究人员之间的合作。该项目旨在利用语言的经典统计三元模型的简单性，同时在语法和语义方面对其进行扩充，这些方面限制了新语法三元模型的使用，使其优于纯随机模型。本研究使用了概率链接语法的概念，将三元语法整合到一个统一的框架中，以计算高效的方式建模远距离语法依赖关系。从熵测量的角度来看，所提出的方法有望比当前方法具有更大的预测能力，并将有限状态自动机模型和新的统计估计算法与现代强大的机器集成在一起，从而改进语音识别，翻译和理解系统。这是美国国家科学基金会/美国国防部高级研究计划署人类语言技术计划持续奖励的第一年，该计划涉及匹兹堡大学和斯坦福研究所之间的合作。该项目涉及研究如何将意图和信息视角整合到架构中，以解释和生成交互式话语。研究的具体问题包括听者对说话人计划的识别，会话记录概念的形式化，从计算角度出发的话语结构，以及涉及数量的含义分析和涉及生成和解释过程之间相互作用的类似现象。研究的基础是在有限状态近似方法和基于知识的系统中使用溯因推理，互动话语中的适应过程，连贯文本的生成，在计划识别中使用可推翻推理，以及实现交际目标的话语规划。这是NSF/ARPA人类语言技术计划持续奖励的第一年，该计划涉及卡内基梅隆大学和SRA公司之间的合作。该项目涉及调查从文本中提取信息的方法，并将其总结为结构化数据记录，以便随后进行自动处理。该方法包括合并、融合和合并在一个或多个时间点处理同一主题的多个文本，并利用结果扩大现有知识库，以及发现数据中的潜在趋势。研究的基本问题包括评估信息一致性和冗余的度量，多个共同引用文本的概率统一，描述随时间演变的事件的文本统一表示的方法，以及用于预测趋势的约束结构归纳。该项目使用ARPA TIPSTER项目中预提取的文本表示，这些文本由于包含遗漏和委托错误而被认为是有噪声的，以便增加项目在单个或不断发展的时间点对事件的信息丰富描述中的可追溯性，并在存在不一致和部分信息的情况下开发健壮的方法。