semantic role chunking combining complementary syntactic views
DESCRIPTION
Semantic Role Chunking Combining Complementary Syntactic Views. Sameer Pradhan, Kadri Hacioglu, Wayne Ward, James H. Martin, Daniel Jurafsky . Center for Spoken Language Research Department of Computer Science University of Colorado at Boulder. Department of Linguistics - PowerPoint PPT PresentationTRANSCRIPT
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Semantic Role Chunking Combining Complementary Syntactic Views
Sameer Pradhan, Kadri Hacioglu, Wayne Ward, James H. Martin, Daniel Jurafsky
Center for Spoken Language Research
Department of Computer ScienceUniversity of Colorado at Boulder
Department of LinguisticsStanford University
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Different Syntactic Views
Hypothesis: Different views make different errors
Two views: Phrase structure based (Charniak, Collins) Chunk based
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Constituents from Charniak parse tree
Charniak Parse Tree
Constituent Views
John kicked the ball .
Collins Parse Tree
Constituents from Collins parse tree
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Chunk View
Salomonwillbuysufficientsharesto coveritsentireposition
OOOB-A2I-A2OB-VB-A1I-A1I-A1
Chunk using an IOB representation [Ramshaw & Marcus, 1995]
Yamcha [Kudo & Matsumoto, 2001]
Bottom up as opposed to top down
Flat representation Uses flat syntactic chunks
[Hacioglu & Ward 2003]
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Algorithm
Generate Charniak and Collins parse based features Add few features from one to the other Generate semantic IOB tags using these views Use them as features Generate the final semantic role label set using a phrase-
based chunking paradigm
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Architecture
Chunker
Charniak Collins Words
Features
IOB
Semantic Role Labels
IOBIOB IOB
Phrases
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Illustration
Train Model
1 2 RB
B
B B B
B B
I
I
I
I
I
I
I
I
IIII
I
I
II
I
O
OO
O
O
O
O
O
O
O
O
O
O
O
B B
O O
Classifier
Model
1 2 HB
B B B
B
B
B
B
B
I
I
I
I
I
I
I
I
I
I
IIII
I
II
I
II
I
O
O
OOO
O
OOO
OO
OOO
OO
OO
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Features
Semantic IOB tags for Charniak and Collins based semantic role labels [Pradhan et al., 2005]
Phrase level chunk features [Hacioglu et al., 2004]
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Active Learning
Randomly selelected 10k examples and trained a NULL vs ARGUMENT classifier
Classified remaining examples using this classifier Added misclassified examples to the seed set Iterated Final data amounted to about a third of the total
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Combination Results
Test : Section 24 of PropBankTrain : Sections 02-21 of PropBank
ID + Class
ASSERTCharniak
System P R F1
80 75 77ASSERTCollins 79 74 76ASSERTCombined
81 76 78
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Results
Section 24
Submitted System
System P R F1
80.9 75.4 78.0
Section 23
P R F1
81.9 73.3 77.4
Brown
P R F1
73.7 61.5 67.1Bug fixed System 81.9 75.1
78.382.9 74.7
78.674.5 63.3
68.4
ID + Class
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Thank You
Arda AQUAINT program contract OCG4423B
NSF grant IS-9978025
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Software
ASSERT (Automatic Statistical SEmantic Role Tagger) Publicly downloadable at http://oak.colorado.edu/assert Downloaded by more than 50 research groups
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Null Filtering
Removed constituents with P(NULL) > 0.9 Removed phrases with P(NULL) > 0.8 after incorporating
context
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Analysis
Active learning using confidence threshold Constituent level instead of Sentence level N-Best Charniak parses
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Features (Constituent)
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Features (Constituent)
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Features (Phrase)
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Features (Phrase)
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Representation
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Features
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Features
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But analysts reckon underlying support for sterling has been eroded by the chancellor 's failure to announce any new policy measures in his Mansion House speech last Thursday
Minipar-based Semantic Labeling
Rule-based dependency parser