Information Extraction Using the Structured Language Model

Abstract

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.