Paper: Learning Arguments and Supertypes of Semantic Relations Using Recursive Patterns

ACL ID P10-1150
Title Learning Arguments and Supertypes of Semantic Relations Using Recursive Patterns
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

A challenging problem in open informa- tion extraction and text mining is the learn- ing of the selectional restrictions of se- mantic relations. We propose a mini- mally supervised bootstrapping algorithm that uses a single seed and a recursive lexico-syntactic pattern to learn the ar- guments and the supertypes of a diverse set of semantic relations from the Web. We evaluate the performance of our algo- rithm on multiple semantic relations ex- pressed using “verb”, “noun”, and “verb prep” lexico-syntactic patterns. Human- based evaluation shows that the accuracy of the harvested information is about 90%. We also compare our results with existing knowledge base to outline the similarities and differences of the granularity and di- versity of the harvested knowledge.