WeproposeanewapproachtoEMlearning of PCFGs. We completely separate the process of EM learning from that of parsing, andfor theformer, weintroduce a new EM algorithm called the gra...
Abstract. The present paper presents a new approach of how to convert Gold-style [4] learning in the limit into stochastically finite learning with high confidence. We illustrate t...
We describe the parser of LEU/2, the Linguistic Experimentation Environment of the LILOG project. The parser is designed to support and encourage experimentation with different gr...
Stochastic context-free grammars (SCFGs) have long been recognized as useful for a large variety of tasks including natural language processing, morphological parsing, speech reco...
Many learning systems suffer from the utility problem; that is, that time after learning is greater than time before learning. Discovering how to assure that learned knowledge wil...