Most current NLP systems have little knowledge about quantitative attributes
of objects and events. We propose an unsupervised method for collecting
quantitative information from large amounts of web data, and use it to create a
new, very large resource consisting of distributions over physical quantities
associated with objects, adjectives, and verbs which we call Distributions over
Quantitative (DoQ). This contrasts with recent work in this area which has
focused on making only relative comparisons such as "Is a lion bigger than a
wolf?". Our evaluation shows that DoQ compares favorably with state of the art
results on existing datasets for relative comparisons of nouns and adjectives,
and on a new dataset we introduce.

本文提出一种无监督的从大量网页数据中收集数量信息的方法，并将其用于创建一个由与物体、形容词和动词相关的物理量分布组成的新的大型资源，即 DoQ。与该领域的最新研究不同的是，DoQ 重点关注绝对的数量比较，而不仅仅是相对比较，例如 “狮子比狼大吗？”。我们的评估表明，DoQ 在名词和形容词的相对比较方面与现有数据集的最新结果相比具有优势，并且得出了一个我们引入的新数据集。