APPLYING A STRUCTURED LANGUAGE MODEL TO INFORMATION EXTRACTION
First Claim
1. A method of training an information extraction system to extract information from a natural language input, comprising:
- generating parses with a structured language model using annotated training data that has semantic constituent labels with semantic constituent boundaries identified;
while generating parses, constraining parses to match the semantic constituent boundaries; and
while generating parses, constraining the parses to match the semantic constituent labels.
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Abstract
One feature of the present invention uses the parsing capabilities of a structured language model in the information extraction process. During training, the structured language model is first initialized with syntactically annotated training data. The model is then trained by generating parses on semantically annotated training data enforcing annotated constituent boundaries. The syntactic labels in the parse trees generated by the parser are then replaced with joint syntactic and semantic labels. The model is then trained by generating parses on the semantically annotated training data enforcing the semantic tags or labels found in the training data. The trained model can then be used to extract information from test data using the parses generated by the model.
45 Citations
8 Claims
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1. A method of training an information extraction system to extract information from a natural language input, comprising:
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generating parses with a structured language model using annotated training data that has semantic constituent labels with semantic constituent boundaries identified; while generating parses, constraining parses to match the semantic constituent boundaries; and while generating parses, constraining the parses to match the semantic constituent labels. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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Specification