  We consider the information extraction framework known as document spanners,
and study the problem of efficiently computing the results of the extraction
from an input document, where the extraction task is described as a sequential
variable-set automaton (VA). We pose this problem in the setting of enumeration
algorithms, where we can first run a preprocessing phase and must then produce
the results with a small delay between any two consecutive results. Our goal is
to have an algorithm which is tractable in combined complexity, i.e., in the
sizes of the input document and the VA; while ensuring the best possible data
complexity bounds in the input document size, i.e., constant delay in the
document size. Several recent works at PODS'18 proposed such algorithms but
with linear delay in the document size or with an exponential dependency in
size of the (generally nondeterministic) input VA. In particular, Florenzano et
al. suggest that our desired runtime guarantees cannot be met for general
sequential VAs. We refute this and show that, given a nondeterministic
sequential VA and an input document, we can enumerate the mappings of the VA on
the document with the following bounds: the preprocessing is linear in the
document size and polynomial in the size of the VA, and the delay is
independent of the document and polynomial in the size of the VA. The resulting
algorithm thus achieves tractability in combined complexity and the best
possible data complexity bounds. Moreover, it is rather easy to describe, in
particular for the restricted case of so-called extended VAs.
