Proceedings of the Seventh International Conference on Language Resources and Evaluation

Automatic Discovery of Semantic Relations using MindNet

, , and

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.


  • 434673 bytes

learning, natural language processing, natural language processing

InProceedings

Downloads: 1874 downloads

UMBC ebiquity