Learning soft domain constraints in a factor graph model for template-based information extraction

The ability to accurately extract key information from textual documents is necessary in several downstream applications e.g., automatic knowledge base population from text, semantic information retrieval, question answering, or text summarization. However, information extraction (IE) systems are far from being errorless and in some cases commit errors that seem obvious to a human expert as they violate common sense or domain knowledge.