Presentation on theme: "Parsing with Compositional Vector Grammars Socher, Bauer, Manning, NG 2013."— Presentation transcript:
Parsing with Compositional Vector Grammars Socher, Bauer, Manning, NG 2013
Problem How can we parse a sentence and create a dense representation of it? – N-grams have obvious problems, most important is sparsity Can we resolve syntactic ambiguity with context? “They ate udon with forks” vs “They ate udon with chicken”
Standard Recursive Neural Net I like green eggs [ Vector(I)] [ Vector(like)] W Main [ Vector(I-like)] Score [ Vector(green)] [ Vector(eggs)] Classifier? W Main [ Vector((I-like)green)]
Syntactically Untied RNN I like green eggs [ Vector(I)] [ Vector(like)] W N,V [ Vector(I-like)] Score [ Vector(green)] [ Vector(eggs)] Classifier W adj,N [ Vector(green-eggs)] First, parse lower level with PCFG N VAdj N
Finding the Best Tree (inference) Want to find the parse tree with the max score (which is the sum all the scores of all sub trees) Too expensive to try every combination Trick: use non-RNN method to select best 200 trees (CKY algorithm). Then, beam search these trees with RNN.
Model Comparisons (WSJ Dataset) (Socher’s Model) F1 for parse labels
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