The Recursive Auto-Associative Memory (RAAM) has come to dominate
connectionist investigations into representing compositional
structure. Although an adequate model when dealing with limited data,
the capacity of RAAM to scale-up to real-world tasks has been
frequently questioned. We outline how (S)RAAM (a variant of RAAM)
produces contextsensitive representations that may be used to aid a
deterministic parsing process. By substituting a symbolic stack in an
existing hybrid parser, we show that (S)RAAM is more than capable of
encoding the real-world data sets employed. We conclude by suggesting
that models, such as (S)RAAM, offer valuable insights into the
features of connectionist compositional representations.