  Information extraction traditionally focuses on extracting relations between
identifiable entities, such as <Monterey, locatedIn, California>. Yet, texts
often also contain Counting information, stating that a subject is in a
specific relation with a number of objects, without mentioning the objects
themselves, for example, "California is divided into 58 counties". Such
counting quantifiers can help in a variety of tasks such as query answering or
knowledge base curation, but are neglected by prior work. This paper develops
the first full-fledged system for extracting counting information from text,
called CINEX. We employ distant supervision using fact counts from a knowledge
base as training seeds, and develop novel techniques for dealing with several
challenges: (i) non-maximal training seeds due to the incompleteness of
knowledge bases, (ii) sparse and skewed observations in text sources, and (iii)
high diversity of linguistic patterns. Experiments with five human-evaluated
relations show that CINEX can achieve 60% average precision for extracting
counting information. In a large-scale experiment, we demonstrate the potential
for knowledge base enrichment by applying CINEX to 2,474 frequent relations in
Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct
relations, which is 28% more than the existing Wikidata facts for these
relations.

Computation and Language