Relational Learning for NLP using Linear Threshold Elements
Roni Khardon, Dan Roth, Leslie Gabriel Valiant · 1999
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 oer 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. 1 Introduction The paper explores some aspects of relational knowledge representation and their learnability. While the discussion is to a large extent general it is made in the context of low-level natural language processing (NLP) tasks. Recent eorts in NLP emphasize empirical approaches, that attempt to learn how to perform various natural language tasks by being trained using an annotated corpus. These approaches have been used for a wide variety of fairly low level tasks such as part-of-speech...