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
this https URL

本文提出了基于选择偏好原则的常识知识采集方法，构建了基于事件（activity，state 和 event）的大规模知识图谱 ASER，并使用概念化模块极大地拓展了其覆盖面。

ASER：通过高阶选择性偏好获取大规模的常识知识

ASER: Towards Large-scale Commonsense Knowledge Acquisition via  Higher-order Selectional Preference over Eventualities

We present a neural framework for learning associations between interrelated
groups of words such as the ones found in Subject-Verb-Object (SVO) structures.
Our model induces a joint function-specific word vector space, where vectors of
e.g. plausible SVO compositions lie close together. The model retains
information about word group membership even in the joint space, and can
thereby effectively be applied to a number of tasks reasoning over the SVO
structure. We show the robustness and versatility of the proposed framework by
reporting state-of-the-art results on the tasks of estimating selectional
preference and event similarity. The results indicate that the combinations of
representations learned with our task-independent model outperform
task-specific architectures from prior work, while reducing the number of
parameters by up to 95%.

我们提出了一个用于学习关联性的神经框架，该框架可应用于主谓宾结构等词组的词向量空间的学习。结果表明，我们的模型在选择偏好和事件相似性等任务上均具有鲁棒性和多功能性，并减少了高达 95％的参数数量。

多方向联合优化功能特定词表示

Multidirectional Associative Optimization of Function-Specific Word  Representations

Selectional Preference (SP) is a commonly observed language phenomenon and
proved to be useful in many natural language processing tasks. To provide a
better evaluation method for SP models, we introduce SP-10K, a large-scale
evaluation set that provides human ratings for the plausibility of 10,000 SP
pairs over five SP relations, covering 2,500 most frequent verbs, nouns, and
adjectives in American English. Three representative SP acquisition methods
based on pseudo-disambiguation are evaluated with SP-10K. To demonstrate the
importance of our dataset, we investigate the relationship between SP-10K and
the commonsense knowledge in ConceptNet5 and show the potential of using SP to
represent the commonsense knowledge. We also use the Winograd Schema Challenge
to prove that the proposed new SP relations are essential for the hard pronoun
coreference resolution problem.

通过介绍 SP-10K，提出一种更好的方法评估 SP 模型，并通过评估三种 SP 获取方法，探讨 SP-10K 与常识知识的关系，证明了提出新的 SP 关系对于处理短语代词共指问题的重要性。