  The paper presents a data-driven approach to information extraction (viewed
as template filling) using the structured language model (SLM) as a statistical
parser. The task of template filling is cast as constrained parsing using the
SLM. The model is automatically trained from a set of sentences annotated with
frame/slot labels and spans. Training proceeds in stages: first a constrained
syntactic parser is trained such that the parses on training data meet the
specified semantic spans, then the non-terminal labels are enriched to contain
semantic information and finally a constrained syntactic+semantic parser is
trained on the parse trees resulting from the previous stage. Despite the small
amount of training data used, the model is shown to outperform the slot level
accuracy of a simple semantic grammar authored manually for the MiPad ---
personal information management --- task.
