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Prototype-Driven Grammar Induction Aria Haghighi and Dan Klein Computer Science Division University of California Berkeley

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Grammar Induction DT NN VBD DT NN IN NN The screen was a sea of red

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First Attempt DT NN VBD DT NN IN NN The screen was a sea of red

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Central Questions How do we specify what we want to learn? How do we fix observed errors? What’s an NP? That’s not quite it!

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Experimental Set-up Binary Grammar { X 1, X 2, … X n } plus POS tags Data WSJ-10 [7k sentences] Evaluate on Labeled F 1 Grammar Upper Bound: 86.1 XjXj X i XkXk

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Experiment Roadmap Unconstrained Induction Need bracket constraint! Gold Bracket Induction Prototypes and Similarity CCM Bracket Induction PhrasePrototype NPDT NN PPIN DT NN

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Unconstrained PCFG Induction Learn PCFG with EM Inside-Outside Algorithm Lari & Young [93] Results 0 i j n (Inside) (Outside)

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Gold Brackets Periera & Schables [93] Result Constrained PCFG Induction

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Encoding Knowledge What’s an NP? Semi-Supervised Learning

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Encoding Knowledge What’s an NP? For instance, DT NN JJ NNS NNP Prototype Learning

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Add Prototypes Manually constructed Grammar Induction Experiments

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How to use prototypes? ? ? ? ? DT The NN koala VBD sat IN in DT the NN tree ¦¦ ? NP PP VP S

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How to use prototypes? NP PP VP DT The NN koala VBD sat IN in DT the NN tree ¦ ¦ S JJ hungry ?NP

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Distributional Similarity Context Distribution (DT JJ NN) = { ¦ __ VBD : 0.3, VBD __ ¦ : 0.2, IN __ VBD: 0.1, ….. } Similarity (DT NN) (DT JJ NN) (JJ NNS) (NNP NNP) NP

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Distributional Similarity Prototype Approximation (NP) ¼ Uniform ( (DT NN), (JJ NNS), (NNP NNP) ) Prototype Similarity Feature span(DT JJ NN) emits proto=NP span(MD NNS) emits proto=NONE

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Prototype CFG + Model NP PP VP DT The NN koala VBD sat IN in DT the NN tree ¦ ¦ S JJ hungry NP P (DT NP | NP) P (proto=NP | NP)

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Prototype CFG+ Induction Experimental Set-Up BLIPP corpus Gold Brackets Results

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Summary So Far Bracket constraint and prototypes give good performance!

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Constituent-Context Model

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Product Model Different Aspects of Syntax CCM : Yield and Context properties CFG : Hierarchical properties Intersected EM [Klein 2005] Encourages mass on trees compatible with CCM and CFG

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Grammar Induction Experiments Intersected CFG and CCM No prototypes Results

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Grammar Induction Experiments Intersected CFG + and CCM Add Prototypes Results

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Reacting to Errors Possessive NPs Our Tree Correct Tree

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Reacting to Errors Add Prototype: NP-POS NN POS New Analysis

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Error Analysis Modal VPs Our TreeCorrect Tree

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Reacting to Errors Add Prototype: VP-INF VB NN New Analysis

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Fixing Errors Supplement Prototypes NP-POS and VP-INF Results

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Results Summary

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Conclusion Prototype-Driven Learning Flexible Weakly Supervised Framework Merged distributional clustering techniques with supervised structured models

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Thank You! http://www.cs.berkeley.edu/~aria42

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Unconstrained PCFG Induction Binary Grammar { X 1, X 2, … X n } Learn PCFG with EM Inside-Outside Algorithm Lari & Young [93] 0 i j n (Inside) (Outside) V XjXj X i XkXk N XkXk XjXj V N

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