Coarse-to-Fine Efficient Viterbi Parsing Nathan Bodenstab OGI RPE Presentation May 8, 2006.

Coarse-to-Fine Efficient Viterbi Parsing

Nathan BodenstabOGI RPE Presentation

May 8, 2006

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Outline

• What is Natural Language Parsing?

• Data Driven Parsing

• Hypergraphs and Parsing Algorithms

• High Accuracy Parsing

• Coarse-to-Fine

• Empirical Results

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What is Natural Language Parsing?

• Provides a sentence with syntactic information by hierarchically clustering and labeling its constituents.

• A constituent is a group of one or more words that function together as a unit.

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What is Natural Language Parsing?

• Provides a sentence with syntactic information by hierarchically clustering and labeling its constituents.

• A constituent is a group of one or more words that function together as a unit.

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Why Parse Sentences?

• Syntactic structure is useful in– Speech Recognition– Machine Translation– Language Understanding

• Word Sense Disambiguation (ex. “bottle”)• Question-Answering• Document Summarization

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Outline





• Coarse-to-Fine


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Data Driven Parsing

• Parsing = Grammar + Algorithm• Probabilistic Context-Free Grammar

P(children=[Determiner, Adjective, Noun] | parent=NounPhrase)

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• Find the maximum likelihood parse tree from all grammatically valid candidates.

• The probability of a parse tree is the product of all its grammar rule (constituent) probabilities.

• The number of grammatically valid parse trees increases exponentially with the length of the sentence.

Data Driven Parsing

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Outline





• Coarse-to-Fine


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Hypergraphs

• A directed hypergraph can facilitate dynamic programming (Klein and Manning, 2001).

• A hyperedge connects a set of tail nodes to a set of head nodes.

Standard Edge Hyperedge

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Hypergraphs

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The CYK Algorithm

• Separates the hypergraph into “levels”• Exhaustively traverses every hyperedge, level by level

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The A* Algorithm

• Maintains a priority queue of traversable hyperedges• Traverses best-first until a complete parse tree is found

Pri

ori

ty Q

ueu

e

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Outline





• Coarse-to-Fine


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High(er) Accuracy Parsing

• Modify the Grammar to include more context• (Grand) Parent Annotation (Johnson, 1998)

P(children=[Determiner, Adjective, Noun] | parent=NounPhrase, grandParent=Sentence)

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Increased Search Space

Original Grammar

Parent AnnotatedGrammar

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Original Grammar


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Original Grammar


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Original Grammar


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Original Grammar


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Grammar Comparison

65

70

75

80

85

90

Ac

cu

rac

y %

• Exact Inference with the CYK algorithm becomes intractable.• Most algorithms using Lexical models resort to greedy search strategies.• We want to find the globally optimal (Viterbi) parse tree for these high- accuracy models efficiently.

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Outline





• Coarse-to-Fine


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Coarse-to-Fine• Efficiently find the optimal parse tree of a large, context-enriched

model (Fine) by following hyperedges suggested by solutions of a simpler model (Coarse).

• To evaluate the feasibility of Coarse-to-Fine, we use – Coarse = WSJ– Fine = Parent

65

70

75

80

85

90

Acc

ura

cy %

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Coarse Grammar

Fine Grammar

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Coarse-to-Fine

Build Coarse hypergraph

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Coarse-to-Fine

Choose a Coarse hyperedge

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Coarse-to-FineReplace the Coarse hyperedge with Fine hyperedge (modifies probability)

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Coarse-to-Fine

Propagate probability difference

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Coarse-to-Fine

Repeat until optimal parse treehas only Fine hyperedges

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Upper-Bound Grammar

• Replacing a Coarse hyperedge with a Fine hyperedge can increase or decrease its probability.

• Once we have found a parse tree with only Fine hyperedges, how can we be sure it is optimal?

• Modify the probability of Coarse grammar rules to be an upper-bound on the probability of Fine grammar rules.

nParentAPNn

,|max

nPFineNn

Coarse APAP max

where N is the set of non-terminals and is a grammar rule.A

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Outline





• Coarse-to-Fine


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ResultsComputational Time

0.001

0.01

0.1

1

10

100

5 7 9 11 13 15 17 19 21 23 25

Sentence Length

Tim

e (s

eco

nd

s)

CTF

CYK

A*

Search Guidance

1

10

100

1000

10000

100000

1000000

10000000

5 7 9 11 13 15 17 19 21 23 25

Sentence Length

Hy

pe

red

ge

s T

rav

ers

ed

CYK

A*

CTF

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Summary & Future Research

• Coarse-to-Fine is a new exact inference algorithm to efficiently traverse a large hypergraph space by using the solutions of simpler models.

• Full probability propagation through the hypergraph hinders computational performance. – Full propagation is not necessary; lower-bound of log2(n)

operations.

• Over 95% reduction in search space compared to baseline CYK algorithm.– Should prune even more space with higher-accuracy (Lexical)

models.

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Thanks

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Choosing a Coarse HyperedgeTop-Down vs. Bottom-Up

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Top-Down vs. Bottom-UpComputational Time Comparison

0

10

20

30

40

50

60

70

80

90

100

5 7 9 11 13 15 17 19 21 23 25

Sentence Length

Tim

e (s

eco

nd

s)

CTF Top-Down

CTF Bottom-Up

Search Guidance Comparison

0

50000

100000

150000

200000

250000

300000

5 7 9 11 13 15 17 19 21 23 25

Sentence Length

Hyp

ered

ges

Tra

vers

ed

CTF Top-Down

CTF Bottom-Up

• Top-Down• Traverses more hyperedges• Hyperedges are closer to the root• Requires less propagation (1/2)

• Bottom-Up• Traverses less hyperedges• Hyperedges are near the leaves (words) and shared by many trees• True probability of trees isn’t know at the beginning of CTF

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Coarse-to-Fine Motivation

Optimal Coarse Tree

Optimal Fine Tree

Coarse-to-Fine Efficient Viterbi Parsing Nathan Bodenstab OGI RPE Presentation May 8, 2006.

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