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.

这个项目的目的是开发概率语言模型，有效地整合指称语义信息到句法和语音识别阶段，用于口语接口传感器或机器人代理。 这种整合的目的是让信息的含义或外延的话在代理的当前环境的背景下，影响概率估计它分配到假设的分析，其输入，在任何识别决策已经作出，使接口代理可以在其搜索有利于那些分析，“有意义”在其表示的当前状态的世界。 由于这些模型可以在可以用于在代理的词典中建立单词含义的相同种类的示例上进行训练，预计他们将更容易适应不断变化的领域比那些完全依赖于字合作，基于此模型的识别器最终将作为评估搜索和救援中网络侦察机器人口语接口的测试平台在与人工智能机器人和视觉实验室的其他成员的联合项目中，以及在与明尼苏达大学空间数据库组成员的联合项目中需要导航数字道路地图或其他地理数据的应用中，应用于家庭护理任务或材料处理应用中的移动的操纵机器人。 该模型还将作为自然语言处理概念教学的框架，为学生提供更广泛的背景，使各种处理组件可以结合在一起，并邀请学生考虑跨越传统组件之间边界的自然语言处理问题的创新解决方案。