  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.
