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

Different Syntactic Views Hypothesis: Different views make different errors Two views: Phrase structure based (Charniak, Collins) Chunk based

Constituents from Charniak parse tree Charniak Parse Tree Constituent Views John kicked the ball. Collins Parse Tree Constituents from Collins parse tree

Chunk View Salomon will buy sufficient shares to cover its entire position O O O B-A2 I-A2 O B-V B-A1 I-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]

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 ChunkerCharniakCollinsWords Features IOB Semantic Role Labels IOB Phrases

Illustration Train Model 1 2R B B B B B BB I I I I I I I I I I II I I I I I O O O O O O O O O O O O O O B B O O

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 N ULL vs A RGUMENT 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 PropBank Train: Sections of PropBank ID + Class ASSERT Charniak System P R F ASSERT Collins ASSERT Combined

Results Section 24 Submitted System System P R F Section 23 P R F Brown P R F Bug fixed System ID + Class

Thank You Arda AQUAINT program contract OCG4423B NSF grant IS

Software ASSERT (Automatic Statistical SEmantic Role Tagger) Publicly downloadable at Downloaded by more than 50 research groups

Null Filtering Removed constituents with P(N ULL ) > 0.9 Removed phrases with P(N ULL ) > 0.8 after incorporating context

Analysis Active learning using confidence threshold Constituent level instead of Sentence level N-Best Charniak parses

Features (Constituent)

Features (Phrase)

Representation

Features

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