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 Stanford University
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Semantic Role Chunking Combining Complementary Syntactic Views Sameer Pradhan, Kadri Hacioglu, Wayne Ward, James H. Martin, Daniel Jurafsky Center for.
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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
Different Syntactic Views
Hypothesis: Different views make different errors
Two views: Phrase structure based (Charniak, Collins) Chunk based
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]
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
Architecture
Chunker
Charniak Collins Words
Features
IOB
Semantic Role Labels
IOBIOB IOB
Phrases
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
Features
Semantic IOB tags for Charniak and Collins based semantic role labels [Pradhan et al., 2005]
Phrase level chunk features [Hacioglu et al., 2004]
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
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
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
Thank You
Arda AQUAINT program contract OCG4423B
NSF grant IS-9978025
Software
ASSERT (Automatic Statistical SEmantic Role Tagger) Publicly downloadable at http://oak.colorado.edu/assert Downloaded by more than 50 research groups
Null Filtering
Removed constituents with P(NULL) > 0.9 Removed phrases with P(NULL) > 0.8 after incorporating
context
Analysis
Active learning using confidence threshold Constituent level instead of Sentence level N-Best Charniak parses
Features (Constituent)
Features (Constituent)
Features (Phrase)
Features (Phrase)
Representation
Features
Features
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