Learning Linear Precedence rules

Authors:
Vladimir Pericliev
Affiliations:
Institute of Mathematics and Computer Science, Bulgarian Academy of Sciences, Sofia, Bulgaria
Venue:
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Year:
1996

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Abstract

A system is described which learns from examples the Linear Precedence rules in an Immediate Dominance/Linear Precedence grammar. Given a particular Immediate Dominance grammar and hierarchies of feature values potentially relevant for linearization (=the system's bias), the learner generates appropriate natural language expressions to be evanaluted as positive or negative by a teacher, and produces as output Linear Precedence rules which can be directly used by the grammar.