CAREER: Integrating denotational meaning into probabilistic language models

职业：将指称意义整合到概率语言模型中

• 批准号: 0447685
• 负责人: William Schuler
• 金额: $ 50万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-05-15 至 2011-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0447685&HistoricalAwards=false
• 关键词: CAREER; Integrating; denotational; meaning; into

The purpose of this project is to develop probabilistic language models that efficiently integrate denotational semantic information into syntactic and phonological stages of recognition for use in spoken language interfaces to sensor or robotic agents. This integration is intended to allow information about the meanings or denotations of words in the agent's current environmental context to influence the probability estimates it assigns to hypothesized analyses of its input, before any recognition decisions have been made, so that the interfaced agent can favor in its search those analyses that "make sense" in its representation of the current state of the world. Since these models can be trained on the same kinds of examples that may be used to establish word meanings in the agent's lexicon, it is expected that they will be easier to adapt to changing domains than those relying exclusively on word co-occurrence statistics in fixed corpora.A recognizer based on this model will eventually serve as a testbed for evaluating spoken language interfaces to networked scout robots in search and rescue applications and mobile manipulation robots in home care tasks or materials handling applications in joint projects with other members of the Artificial Intelligence Robotics and Vision Lab, as well as in applications that require navigating digital road maps or other geographic data in joint projects with members of the Spatial Databases group at the University of Minnesota. This model will also be adapted as a framework for teaching natural language processing concepts, providing students with a broader context in which various processing components may fit together, and inviting students to consider innovative solutions to natural language processing problems that cross the boundaries among traditional components.

该项目的目的是开发概率语言模型，将指称语义信息有效地集成到识别的句法和语音阶段，以用于传感器或机器人代理的口语界面。 这种集成的目的是在做出任何识别决策之前，允许有关代理当前环境上下文中单词的含义或表示的信息影响其分配给其输入的假设分析的概率估计，以便接口代理可以支持它搜索那些在描述世界当前状况时“有意义”的分析。 由于这些模型可以在可用于在代理词典中建立单词含义的相同类型的示例上进行训练，因此预计它们将比那些完全依赖于固定环境中单词共现统计的模型更容易适应不断变化的领域。基于该模型的识别器最终将作为一个测试平台，用于评估搜索和救援应用中的网络侦察机器人以及家庭护理任务中的移动操纵机器人或与人工智能其他成员的联合项目中的材料处理应用的口语界面机器人技术和视觉实验室，以及需要在与明尼苏达大学空间数据库小组成员的联合项目中导航数字路线图或其他地理数据的应用程序。 该模型还将被改编为教授自然语言处理概念的框架，为学生提供更广泛的背景，其中各种处理组件可以组合在一起，并邀请学生考虑跨越传统组件之间界限的自然语言处理问题的创新解决方案。