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。小说的理念是从各种文本(包括小说)中的具体断言中抽象出关于世界上什么是可能的一般命题。这将通过对句法分析树的成分解释、启发式方法来决定动词论元结构以及伴随而来的逻辑形式的简化和抽象来完成。研究将使用最先进的解析器来从未注释的文本中获取一般知识。词语的语义分类将加强和消除从文本中获得的知识的歧义。将强调对因果知识的提取，因为这是常识理解世界的核心。这将通过使用逻辑形式的事件变量和对状语修饰的更全面的解释来实现。研究还将调查一般知识(通常是不确定的或甚至不一致的)的表示，以及使用这些知识进行推理的方法。其中一个成果将是一个演示系统，它能够回答引起常识的简单问题。这项工作的更广泛影响包括加强学习和本科生以及博士生的积极研究参与。通过帮助突破知识获取瓶颈，这项研究将为为广泛的潜在应用构建更用户友好的人工智能系统铺平道路，例如在用户和更专门的软件、医疗咨询系统、教学系统和电脑游戏之间进行调解的个人代理。它还可以帮助引导自然语言理解系统向更接近人类的理解方向发展。

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.