String transformation systems have been introduced in (Brill, 1995) and have several applications in natural language processing. In this work we consider the computational problem of automatically learning from a given corpus the set of transformations presenting the best evidence. We introduce an original data structure and efficient algorithms that learn some families of transformations that are relevant for part-of-speech tagging and phonological rule systems. We also show that the same learning problem becomes NP-hard in cases of an unbounded use of don't care symbols in a transformation.
String Transformation Learning
SATTA, GIORGIO;
1997
Abstract
String transformation systems have been introduced in (Brill, 1995) and have several applications in natural language processing. In this work we consider the computational problem of automatically learning from a given corpus the set of transformations presenting the best evidence. We introduce an original data structure and efficient algorithms that learn some families of transformations that are relevant for part-of-speech tagging and phonological rule systems. We also show that the same learning problem becomes NP-hard in cases of an unbounded use of don't care symbols in a transformation.
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