Deriving General World Knowledge from Texts by Abstraction of Logical Forms

该项目的目标是突破知识获取瓶颈，努力赋予人工智能（AI）系统常识。 新颖的想法是从各种文本（包括小说）中的具体断言中抽象出关于世界上可能发生的事情的一般命题。 这将通过分析树的组合解释来完成，用语法学来决定动词论元结构，并伴随着逻辑形式的简化和抽象。这项研究将使用最先进的分析器从未注释的文本中获得一般知识。 词汇的语义分类将加强和消除来自文本的知识。 我们将强调因果知识的提取，因为这对于常识性地理解世界至关重要。 这将通过使用逻辑形式的事件变量和对状语修饰的更充分的解释而成为可能。 研究还将调查一般（通常是不确定的，甚至不一致的）知识的表示和使用这些知识进行推理的方法。 其成果之一将是一个能够回答引出一般知识的简单问题的演示系统，这项工作的更广泛影响包括促进博士生和本科生的学习和积极参与研究。 通过帮助突破知识获取的瓶颈，这项研究将为构建更用户友好的人工智能系统铺平道路，这些系统可以用于广泛的潜在应用，例如在用户和更专业的软件之间进行调解的个人代理，医疗建议系统，辅导系统和计算机游戏。 它还可以帮助自然语言理解系统实现更人性化的理解。

The goal of this project is to break through the knowledge acquisition bottleneck in the effort to endow artificial intelligence (AI) systems with common sense. The novel idea is to abstract general propositions about what is possible in the world from the specific assertions made in miscellaneous texts (including fiction). This will be accomplished by compositional interpretation of parse trees, with heuristics to decide on verb argument structure and with concomitant simplification and abstraction of logical forms.The research will employ state-of-the-art parsers to derive general knowledge from unannotated texts. Semantic classifications of words will strengthen and disambiguate the knowledge derived from text. There will be an emphasis on the extraction of causal knowledge, since this is so central to commonsense understanding of the world. This will be made possible by the use of event variables in logical forms and fuller interpretation of adverbial modification. Research will also investigate the representation of general (and often uncertain or even inconsistent) knowledge and methods of using such knowledge for inference. One result will be a demonstration system that is able to answer simple questions eliciting general knowledge.The broader impact of this work includes enhanced learning and active research participation by doctoral candidates as well as undergraduates. By helping to break through the knowledge acquisition bottleneck, this research will pave the way for building more user-friendly AI systems for a broad range of potential applications, such as personal agents that mediate between a user and more specialized software, medical advice systems, tutoring systems, and computer games. It could also serve to bootstrap natural language understanding systems towards more human-like understanding.