  We provide the first computational treatment of fused-heads constructions
(FH), focusing on the numeric fused-heads (NFH). FHs constructions are noun
phrases (NPs) in which the head noun is missing and is said to be `fused' with
its dependent modifier. This missing information is implicit and is important
for sentence understanding. The missing references are easily filled in by
humans but pose a challenge for computational models. We formulate the handling
of FH as a two stages process: identification of the FH construction and
resolution of the missing head. We explore the NFH phenomena in large corpora
of English text and create (1) a dataset and a highly accurate method for NFH
identification; (2) a 10k examples (1M tokens) crowd-sourced dataset of NFH
resolution; and (3) a neural baseline for the NFH resolution task. We release
our code and dataset, in hope to foster further research into this challenging
problem.
