University of Texas at Austin Machine Learning Group Machine Learning Group Department of Computer Sciences University of Texas at Austin Learning Semantic Parsers Using Statistical Syntactic Parsing Techniques February 8, 2006 Ruifang Ge Supervisor Professor: Raymond J. Mooney
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Learning Semantic Parsers Using Statistical Syntactic Parsing Techniques
Learning Semantic Parsers Using Statistical Syntactic Parsing Techniques. Ruifang Ge Supervisor Professor: Raymond J. Mooney. February 8, 2006. Semantic Parsing. - PowerPoint PPT Presentation
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University of Texas at Austin
Machine Learning Group
Machine Learning GroupDepartment of Computer Sciences
University of Texas at Austin
Learning Semantic Parsers Using Statistical Syntactic Parsing Techniques
February 8, 2006
Ruifang Ge
Supervisor Professor: Raymond J. Mooney
2
Semantic Parsing
• Semantic Parsing: maps a natural-language sentence to a complete, detailed and formal meaning representation (MR) in a meaning representation language
• Applications– Core component in practical spoken language systems:
• JUPITER (MIT weather 1-888-573-talk)
• MERCURY (MIT flight 1-877-MIT-talk)
– Advice taking (Kuhlmann et al., 2004)
3
CLang: RoboCup Coach Language
• In RoboCup Coach competition teams compete to coach simulated players
• The coaching instructions are given in a formal language called CLang
Simulated soccer field
Coach
CLang
If our player 2 has the ball, our player 4
should stay in our half
((bowner our {2})
(do our {4} (pos (half our))))
Semantic Parsing
4
Motivating Example
Semantic parsing is a compositional process. Sentence structures are needed for building meaning representations.
((bowner our {2}) (do our {4} (pos (half our))))
If our player 2 has the ball, our player 4 should stay in our half
5
Roadmap
• Related work on semantic parsing• SCISSOR• Experimental results• Proposed work• Conclusions
6
Category I: Syntax-Based Approaches
• Meaning composition follows the tree structure of a syntactic parse
• Composing the meaning of a constituent from the meanings of its sub-constituents in a syntactic parse – specified using syntactic relations and semantic
constraints in application domains
• Miller et al. (1996), Zettlemoyer & Collins (2005)
7
Category I: Example
our player 2 has
the ball
PRP$-our NN-player(_,_) CD-2 VB-bowner(_)
DT-null NN-null
NP-null
VP-bowner(_)NP-player(our,2)
S-bowner(player(our,2))
player(team,unum) semantic vacuous
require argumentsrequire no arguments
bowner(player)
8
Category I: Example
our player 2 has
the ball
PRP$-our NN-player(_,_) CD-2 VB-bowner(_)
DT-null NN-null
NP-null
VP-bowner(_)
S-bowner(player(our,2))
NP-player(our,2)
player(team,unum)
bowner(player)
9
Category I: Example
our player 2 has
the ball
PRP$-our NN-player(_,_) CD-2 VB-bowner(_)
DT-null NN-null
NP-null
VP-bowner(_)NP-player(our,2)
S-bowner(player(our,2))
player(team,unum)
bowner(player)
10
Category II: Purely Semantic-Driven Approaches
• No syntactic information is used in building tree structures
• Non-terminals in this category correspond to semantic concepts in application domains
• Tang & Mooney (2001), Kate (2005), Wong(2005)
11
Category II: Example
our player 2
has the ballour 2
player
bowner
12
Category III: Hybrid Approaches
• Utilizing syntactic information in semantic parsing approaches driven by semantics– Syntactic phrase boundaries
– syntactic category of semantic concepts
– word dependencies
• Kate, Wong & Mooney (2005)
13
Our Approach
• We introduce an approach falling into category I: a syntax-driven approach
techniques to help building tree structures for meaning composition
– State-of-the-art statistical parsing techniques are becoming more and more robust and accurate [Collins (1997) and Charniak & Johnson (2005)]
14
Roadmap
• Related work on semantic parsing• SCISSOR• Experimental results• Proposed work• Conclusions
15
SCISSOR: Semantic Composition that Integrates Syntax and Semantics to get Optimal Representations
16
• An integrated syntax-based approach – Allows both syntax and semantics to be used
simultaneously to build meaning representations
• A statistical parser is used to generate a semantically augmented parse tree (SAPT)
• Translate a SAPT into a complete formal meaning representation (MR) using a meaning composition process
SCISSOR
MR: bowner(player(our,2))
our player 2 has
the ball
PRP$-team NN-player CD-unum VB-bowner
DT-null NN-null
NP-null
VP-bownerNP-player
S-bowner
17
• An integrated syntax-based approach – Allows both syntax and semantics to be used
simultaneously to build meaning representations
• A statistical parser is used to generate a semantically augmented parse tree (SAPT)
• Translate a SAPT into a complete formal meaning representation (MR) using a meaning composition process
• Allow statistical modeling of semantic selectional constraints in application domains– (AGENT pass) = PLAYER
SCISSOR
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Overview of SCISSOR
Integrated Semantic ParserSAPT Training Examples
TRAINING
SAPT
ComposeMR
MR
NL Sentence
TESTING
learner
19
Extending Collins’ (1997) Syntactic Parsing Model
• Collins’ (1997) introduced a lexicalized head-driven syntactic parsing model
• Bikel’s (2004) provides an easily-extended open-source version of the Collins statistical parser
• Extending the parsing model to generate semantic labels simultaneously with syntactic labels constrained by semantic constraints in application domains