Using ILP to Construct Features for Information Extraction from Semi-structured Text

Machine-generated documents containing semi-structured text are rapidly forming the bulk of data being stored in an organisation. Given a feature-based representation of such data, methods like SVMs are able to construct good models for information extraction (IE). But how are the feature-definitions to be obtained in the first place? (We are referring here to the representation problem: selecting good features from the ones defined comes later.) So far, features have been defined manually or by using special-purpose programs: neither approach scaling well to handle the heterogeneity of the data or new domain-specific information. We suggest that Inductive Logic Programming (ILP) could assist in this. Specifically, we demonstrate the use of ILP to define features for seven IE tasks using two disparate sources of information. Our findings are as follows: (1) the ILP system is able to identify efficiently large numbers of good features. Typically, the time taken to identify the f...

• Type: Conference
• Year: 2007
• Where: ILP
• Authors: Ganesh Ramakrishnan, Sachindra Joshi, Sreeram Balakrishnan, Ashwin Srinivasan
• Keywords: Artificial Intelligence, Feature-based Representation, IE Tasks, ILP 2007, Information Extraction