Information extraction deals with extracting entities (such as people, organizations or locations) and named relations between entities (such as ""People born-in Country"") from text documents. An important challenge in information extraction is the labeling of training data which is usually done manually and is therefore very laborious and in certain cases impractical. This paper introduces a new “model” to extract semantic relations fully automatically from text using the Encarta encyclopedia and lexical-semantic relations discovered by MindNet. MindNet is a lexical knowledge base that can be constructed fully automatically from a given text corpus without any human intervention. Encarta articles are categorized and linked to related articles by experts. We demonstrate how the structured data available in Encarta and the lexical semantic relations between words in MindNet can be used to enrich MindNet with semantic relations between entities. With a slight trade off of accuracy a semantically enriched MindNet can be used to extract relations from a text corpus without any human intervention.

In this paper we present a proposal to help bypass the bottleneck of knowledge-based systems working under the assumption that the knowledge sources are complete. We show how to create, on the fly, new lexicon entries using lexico-semantic rules and how to create new concepts for unknown words, investigating a new linguistically-motivated model to trigger concepts in context.