Natural Language Grammar Induction Using a Constituent-Context Model

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Natural Language Processing (Almost) from Scratch (2011, background): “However, Klein and Manning (2002) describe a rare example of realistic unsupervised grammar induction using a cross-entropy approach on binary-branching parsing trees, that is, by forcing the system to generate a hierarchical representation. ar Xi v It is therefore desirable to define alternative…”
Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing - eScholarship (2007, background): “This includes many standard models of probability distributions defined on graphs – for example, graphs with fixed structure, such as MRF’s [2] or Conditional Random Fields [3], or Probabilistic Context Free Grammars (PCFG’s) [4] where the structure is variable.”
Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency (2004, result): “Comparative figures from other experiments show that it also outperforms the other wide-coverage tree induction systems for which we had treebank parsing results (Klein and Manning, 2001).”
Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing (2006, background): “This includes many standard models of probability distributions defined on graphs – for example, graphs with fixed structure, such as MRF’s [2] or Conditional Random Fields [3], or Probabilistic Context Free Grammars (PCFG’s) [4] where the structure is variable.”