Relational learning for NLP using linear threshold elements

Roni Khardon, Dan Roth, Leslie G. Valiant

Research output: Contribution to journalConference articlepeer-review


We describe a coherent view of learning and reasoning with relational representations in the context of natural language processing. In particular, we discuss the Neuroidal Architecture, Inductive Logic Programming and the SNoW system explaining the relationships among these, and thereby offer an explanation of the theoretical basis for the SNoW system. We suggest that extensions of this system along the lines suggested by the theory may provide new levels of scalability and functionality.

Original languageEnglish (US)
Pages (from-to)911-917
Number of pages7
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 1999
Event16th International Joint Conference on Artificial Intelligence, IJCAI 1999 - Stockholm, Sweden
Duration: Jul 31 1999Aug 6 1999

ASJC Scopus subject areas

  • Artificial Intelligence


Dive into the research topics of 'Relational learning for NLP using linear threshold elements'. Together they form a unique fingerprint.

Cite this