ASER：通过高阶选择偏好获取大规模常识知识获取
ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities
摘要：
常识知识获取和推理一直是人工智能的核心问题。然而，在过去，一直缺乏可扩展的方法来收集常识知识。在本文中，我们建议制定原则，收集常识知识的基础上选择性偏好。我们将选择偏好的定义从单跳语言句法关系推广到语言图上的高阶关系。不像以前的常识知识定义（例如，ConceptNet），选择偏好（SP）知识仅依赖于语言图上的统计分布，利用现代工具可以从无标注语料库中高效准确地获取SP知识。遵循这一原则，我们开发了一个大规模的可能性（一个涵盖活动，状态和事件的语言术语）的知识图谱ASER，其中每个可能性表示为依赖图，它们之间的关系是一个浅的话语分析中定义的话语关系。对所收集的语言图的高阶选择偏好反映了各种常识性知识。此外，动机的观察，人类理解事件抽象的观察到的事件到一个更高的水平，从而可以将他们的知识转移到新的事件，我们提出了一个概念化模块，以显着提高ASER的覆盖面。总的来说，ASER在4.38亿个可能性之间包含6.48亿条边。概念化后的Probase，一个选择偏好的概念实例关系知识库，我们的概念图包含15万个概念化的可能性和2.24亿之间的边缘。提供详细的分析，以证明其质量。所有收集的数据、API和工具都可以在www.example.com上找到。
英文摘要：
Commonsense knowledge acquisition and reasoning have long been a core artificial intelligence problem. However, in the past, there has been a lack of scalable methods to collect commonsense knowledge. In this paper, we propose to develop principles for collecting commonsense knowledge based on selectional preference. We generalize the definition of selectional preference from one-hop linguistic syntactic relations to higher-order relations over linguistic graphs. Unlike previous commonsense knowledge definition (e.g., ConceptNet), selectional preference (SP) knowledge only relies on statistical distribution over linguistic graphs, which can be efficiently and accurately acquired from the unlabeled corpus with modern tools. Following this principle, we develop a large-scale eventuality (a linguistic term covering activity, state, and event)-based knowledge graph ASER, where each eventuality is represented as a dependency graph, and the relation between them is a discourse relation defined in shallow discourse parsing. The higher-order selectional preference over collected linguistic graphs reflects various kinds of commonsense knowledge. Moreover, motivated by the observation that humans understand events by abstracting the observed events to a higher level and can thus transfer their knowledge to new events, we propose a conceptualization module to significantly boost the coverage of ASER. In total, ASER contains 648 million edges between 438 million eventualities. After conceptualization with Probase, a selectional preference based concept-instance relational knowledge base, our concept graph contains 15 million conceptualized eventualities and 224 million edges between them. Detailed analysis is provided to demonstrate its quality. All the collected data, APIs, and tools are available at https://github.com/HKUST-KnowComp/ASER.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
