There are several linguistic claims about situations where words are more
likely to be used as metaphors. However, few studies have sought to verify such
claims with large corpora. This study entails a large-scale, corpus-based
analysis of certain existing claims about verb metaphors, by applying metaphor
detection to sentences extracted from Common Crawl and using the statistics
obtained from the results. The verification results indicate that the direct
objects of verbs used as metaphors tend to have lower degrees of concreteness,
imageability, and familiarity, and that metaphors are more likely to be used in
emotional and subjective sentences.

这项研究是基于大规模语料库的分析，验证了有关动词隐喻的一些存在观点，结果显示，作为隐喻使用的动词的直接宾语往往具有较低的具体性、可形象化和熟悉度，隐喻更容易在情感和主观句子中使用。

使用大规模自动隐喻辨识验证隐喻断言

Verifying Claims About Metaphors with Large-Scale Automatic Metaphor  Identification

Graphs are used to model interactions in a variety of contexts, and there is
a growing need to quickly assess the structure of a graph. Some of the most
useful graph metrics, especially those measuring social cohesion, are based on
triangles. Despite the importance of these triadic measures, associated
algorithms can be extremely expensive. We propose a new method based on wedge
sampling. This versatile technique allows for the fast and accurate
approximation of all current variants of clustering coefficients and enables
rapid uniform sampling of the triangles of a graph. Our methods come with
provable and practical time-approximation tradeoffs for all computations. We
provide extensive results that show our methods are orders of magnitude faster
than the state-of-the-art, while providing nearly the accuracy of full
enumeration. Our results will enable more wide-scale adoption of triadic
measures for analysis of extremely large graphs, as demonstrated on several
real-world examples.

本文提出了一种名为楔采样的新方法，它可以用于快速与准确地近似计算图中各种变体的聚类系数，并能够快速均匀地采样图中的三角形，其方法具有证明的可行性及性能交换，该方法比现有技术快数个数量级，同时几乎提供完全枚举的准确性，这将有助于广泛采用三角度量分析极大的图形。