CAREER: Integrating denotational meaning into probabilistic language models

这个项目的目的是开发概率语言模型，有效地将指称语义信息整合到识别的句法和语音阶段，用于与传感器或机器人代理的口语接口。这种集成旨在允许智能体当前环境上下文中有关单词含义或外延的信息，在做出任何识别决策之前，影响其分配给其输入的假设分析的概率估计，以便接口智能体可以在其搜索中支持那些在其对当前世界状态的表示中“有意义”的分析。由于这些模型可以在同一类型的例子上进行训练，这些例子可能用于在智能体的词典中建立词义，因此预计它们将比那些完全依赖固定语料库中的词共出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、家庭护理任务中的移动操作机器人或与人工智能机器人和视觉实验室的其他成员联合项目中的材料处理应用的口语接口。以及在与明尼苏达大学空间数据库小组成员的联合项目中需要导航数字路线图或其他地理数据的应用程序。这个模型也将被改编为教授自然语言处理概念的框架，为学生提供一个更广泛的背景，在这个背景中，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边界的自然语言处理问题的创新解决方案。

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.