Stacked Generalization for Information Extraction

1 This paper defines a new stacked generalization framework in the context of information extraction (IE) from online sources. The proposed setting removes the constraint of applying classifiers at the base-level. A set of IE systems are trained instead to identify relevant fragments within text documents, which differs significantly from the task of classifying candidate text fragments as relevant or not. The templates filled by the base-level IE systems are stacked, forming a set of feature vectors for training a metalevel classifier. Thus, base-level IE systems are combined with a common classifier at meta-level. The proposed framework was evaluated on three Web domains, using well known IE approaches at base-level and a variety of classifiers at meta-level. Results demonstrate the added value obtained by combining the base-level IE systems in the new framework.

• Type: Conference
• Year: 2004
• Where: ECAI
• Authors: Georgios Sigletos, Georgios Paliouras, Constantine D. Spyropoulos, Takis Stamapoulos
• Keywords: Artificial Intelligence, Base-level Ie Systems, ECAI 2004, IE Systems, Stacked Generalization Framework