Download presentation

Presentation is loading. Please wait.

Published bySamir Stephens Modified over 2 years ago

1
Prototype-Driven Grammar Induction Aria Haghighi and Dan Klein Computer Science Division University of California Berkeley

2
Grammar Induction DT NN VBD DT NN IN NN The screen was a sea of red

3
First Attempt DT NN VBD DT NN IN NN The screen was a sea of red

4
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!

5
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

6
Experiment Roadmap Unconstrained Induction Need bracket constraint! Gold Bracket Induction Prototypes and Similarity CCM Bracket Induction PhrasePrototype NPDT NN PPIN DT NN

7
Unconstrained PCFG Induction Learn PCFG with EM Inside-Outside Algorithm Lari & Young [93] Results 0 i j n (Inside) (Outside)

8
Gold Brackets Periera & Schables [93] Result Constrained PCFG Induction

9
Encoding Knowledge What’s an NP? Semi-Supervised Learning

10
Encoding Knowledge What’s an NP? For instance, DT NN JJ NNS NNP Prototype Learning

11
Add Prototypes Manually constructed Grammar Induction Experiments

12
How to use prototypes? ? ? ? ? DT The NN koala VBD sat IN in DT the NN tree ¦¦ ? NP PP VP S

13
How to use prototypes? NP PP VP DT The NN koala VBD sat IN in DT the NN tree ¦ ¦ S JJ hungry ?NP

14
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

15
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

16
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)

17
Prototype CFG+ Induction Experimental Set-Up BLIPP corpus Gold Brackets Results

18
Summary So Far Bracket constraint and prototypes give good performance!

19
Constituent-Context Model

20
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

21
Grammar Induction Experiments Intersected CFG and CCM No prototypes Results

22
Grammar Induction Experiments Intersected CFG + and CCM Add Prototypes Results

23
Reacting to Errors Possessive NPs Our Tree Correct Tree

24
Reacting to Errors Add Prototype: NP-POS NN POS New Analysis

25
Error Analysis Modal VPs Our TreeCorrect Tree

26
Reacting to Errors Add Prototype: VP-INF VB NN New Analysis

27
Fixing Errors Supplement Prototypes NP-POS and VP-INF Results

28
Results Summary

29
Conclusion Prototype-Driven Learning Flexible Weakly Supervised Framework Merged distributional clustering techniques with supervised structured models

30
Thank You! http://www.cs.berkeley.edu/~aria42

31
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

Similar presentations

OK

CS626-449: Speech, NLP and the Web/Topics in AI Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture-16: Probabilistic parsing; computing probability of.

CS626-449: Speech, NLP and the Web/Topics in AI Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture-16: Probabilistic parsing; computing probability of.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Free ppt on types of houses Maldi ms ppt online Ppt on history of vedic maths Ppt on abstract art gallery Ppt on integrated child development scheme Ppt on shell structures examples Download ppt on pulse code modulation sampling Ppt on astronomy and astrophysics letters Ppt on nitrogen cycle and nitrogen fixation bacteria Ppt on library management system using rfid