The paper presents a data-driven approach to infor-

mation extraction (viewed as template filling) using

the structured language model (SLM) as a statistical

parser. The task of template filling is cast as con-

strained parsing using the SLM. The model is auto-

matically trained from a set of sentences annotated

with frame/slot labels and spans. Training pro-

ceeds 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 pre-

vious 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 infor-

mation management | task.

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In: Proc. of the Int. Conf. on Empirical Methods in Natural Language Processing

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