Learning and Inference for Information Extraction

Information extraction is a process that extracts limited semantic concepts from text documents and presents them in an organized way. Unlike several other natural language tasks, information extraction has a direct impact on end-user applications. Despite its importance, information ex traction is still a difficult task due to the inherent complexity and ambiguity of human languages. Moreover, mutual dependencies between local predictions of the target concepts further increase difficulty of the task. In order to enhance information extraction technologies, we develop general approaches for two aspects—relational feature generation and global inference with classifiers. It has been quite convincingly argued that relational learning is suitable in training a complicated natural language system. We propose a relational feature generation approach that facilitates relational learning through propositional learning algorithms. In particular, we develop a relational representation language to produce features in a data driven way. The resulting features capture the relational structures of a given domain, and therefore allow the learning algorithms to effectively learn the relational definitions of target concepts. Although the learned classifier can be used to directly predict the target concepts, conflicts between the labels of different target variables often occur due to imperfect classifiers. We propose an inference framework to correct mistakes of the local predictions by using the predictions and task-dependent constraints to produce the best global assignment. This inference framework can be modeled by a Bayesian network or integer linear programming. The proposed learning and inference frameworks have been applied to a variety of information extraction tasks, including entity extraction, entity/relation recognition, and semantic role labeling.