Chinese Semantic Role Labeling with Dependency-driven Constituent Parse Tree Structure
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计算机科学与技术学院
Chinese Semantic Role Labeling with Dependency-driven Constituent Parse Tree Structure
Hongling Wang, Bukang Wang Guodong Zhou
NLP Lab, School of Computer Science & Technology, Soochow University
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Outlines
Motivation Related work Tree kernel-based nominal SRL on D-CPT Experimentation and Results Conclusion
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Motivation(1)
Two kinds of method for SRL Feature-based VS. Tree Kernel-based
The latter has the potential in better capturing structured knowledge in the parse tree structure.
There are only a few studies employing tree kernel-based methods for SRL and most of them focus on the CPT structure.
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Motivation(2)
DPT (Dependency Parse Tree) captures the dependency relationship between individual words, while CPT (Constituent Parse Tree) concerns with the phrase structure in a sentence.
CPT and DPT may behave quite differently in capturing different aspects of syntactic phenomena.
We explore a tree kernel-based methods using a new syntactic parse tree structure, called dependency-driven constituent parse tree (D-CPT) for Chinese nominal SRL.
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Related Work
Some studies use tree kernel-based methods for verbal SRL Moschitti et al., Che et al., Zhang et al.
There are a few related studies in other NLP tasks employing DPT in tree kernel-based methods and achieve comparable performance to the ones on CPT. semantic relation extraction and co-reference resolution
To our knowledge, there are no reported studies on tree kernel-based methods for SRL from the DPT structure perspective.
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D-CPT Structure
The new D-CPT structure benefits from the advantages of both DPT and CPT since D-CPT not only keeps the dependency relationship information in DPT but also retains the basic structure of CPT.
This is done by transforming the DPT structure to a new CPT-style structure, using dependency types instead of phrase labels in the traditional CPT structure.
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Example of DPT with nominal predicate and its related arguments annotated
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Example of achieving D-CPT structure from DPT structure
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Extraction schemes
1) Shortest path tree (SPT) This extraction scheme only includes the nodes
occurring in the shortest path connecting the predicate and the argument candidate, via the nearest commonly-governing node.
2) SV-SPT This extraction scheme includes the support verb
information in nominal SRL.3) H-SV-SPT
Both of the head argument and the support verb information are included in nominal SRL.
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Extraction schemes
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Kernels
In order to capture the complementary nature between feature-based methods and tree kernel-based methods, we combine them via a composite kernel.
Our composite kernel is combined by linearly interpolating a convolution tree kernel KT over a parse tree structure and a feature-based linear kernel KL as follow:
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Experiment setting
Corpus: Chinese NomBank Training data: 648 files (chtb_081 to 899.fid) Test data: 72 files (chtb_001 to 040.fid and chtb_900 t
o 931.fid) Development data: 40 files (chtb_041 to 080.fid)
Tools Classifier: SVM-light toolkit with the convolution tree k
ernel function SVMlight–TK C (SVM) and λ (tree kernel) are fine-tuned to 4.0 and
0.5 respectively
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Results on golden parse trees (1)
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Results on golden parse trees (2)
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Results on automatic parse trees
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Results on Comparison experments(1)
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Results on Comparison experments(2)
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Results on the CoNLL-2009 Chinese corpus
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Conclusion
This paper systematically explores a tree kernel-based method on a novel D-CPT
We propose a simple strategy, which transforms the DPT structure into a CPT-style structure .
Several extraction schemes are designed to extract various kinds of necessary information for nominal SRL and verbal SRL
Evaluation shows the effectiveness of D-CPT both on Chinese NomBank and CoNLL-2009 corpus.
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Thanks!
Q & C?