Abstract
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 Distribution over Quantities (DOQ)1.
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