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Effective bilingual constraints for semisupervised learning of named entity recognizers
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In: http://www.cs.stanford.edu/people/mengqiu/publication/aaai13.pdf (2013)
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Improving semantic role labeling with word sense
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In: http://www.aclweb.org/anthology-new/N/N10/N10-1030.pdf (2010)
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Multilingual Dependency-based Syntactic and Semantic Parsing
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In: http://ufal.mff.cuni.cz/conll2009-st/papers/15_conll09st.pdf (2009)
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Multilingual dependency-based syntactic and semantic parsing
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In: http://www.mt-archive.info/CoNLL-2009-Che.pdf (2009)
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Using a Hybrid Convolution Tree Kernel for Semantic Role Labeling
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In: http://ir.hit.edu.cn/~car/papers/talip.pdf (2008)
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Semantic role labeling using a grammar-driven convolution tree kernel. IEEE transactions on audio, speech, and language processing 16(7):1315
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In: http://ir.hit.edu.cn/ir_papers/2008/Semantic Role Labeling Using a Grammar-Driven Convolution Tree Kernel.pdf (2008)
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Abstract:
Abstract—Convolution tree kernel has shown promising results in semantic role labeling (SRL). However, this kernel does not consider much linguistic knowledge in kernel design and only performs hard matching between subtrees. To overcome these constraints, this paper proposes a grammar-driven convolution tree kernel for SRL by introducing more linguistic knowledge. Compared with the standard convolution tree kernel, the proposed grammar-driven kernel has two advantages: 1) grammar-driven approximate substructure matching, and 2) grammar-driven approximate tree node matching. The two approximate matching mechanisms enable the proposed kernel to better explore linguistically motivated structured knowledge. Experiments on the CoNLL-2005 SRL shared task and the PropBank I corpus show that the proposed kernel outperforms the standard convolution tree kernel significantly. Moreover, we present a composite kernel to integrate a feature-based polynomial kernel and the proposed grammar-driven convolution tree kernel for SRL. Experimental results show that our composite kernel-based method significantly outperforms the previously best-reported ones. Index Terms—Dynamic programming, grammar-driven convolution tree kernel, natural languages, semantic role labeling. I.
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Keyword:
such as WHO did WHAT to WHOM; WHEN; WHERE; WHY
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.9024 http://ir.hit.edu.cn/ir_papers/2008/Semantic Role Labeling Using a Grammar-Driven Convolution Tree Kernel.pdf
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Type-Supervised Domain Adaptation for Joint Segmentation and POS-Tagging
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In: http://aclweb.org/anthology/E/E14/E14-1062.pdf
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Joint Optimization for Chinese POS Tagging and Dependency Parsing
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In: http://ir.hit.edu.cn/~car/papers/ieee14.pdf
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Micro blogs Oriented Word Segmentation System
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In: http://www.aclweb.org/anthology/W/W12/W12-6316.pdf
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HIT-IR-WSD: A WSD System for English Lexical Sample Task
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In: http://acl.ldc.upenn.edu/w/w07/W07-2034.pdf
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Using a Hybrid Convolution Tree Kernel for Semantic Role Labeling
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In: http://www.comp.nus.edu.sg/~tancl/Papers/TALIP/TALIP-Che.pdf
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Multilingual Dependency-based Syntactic and Semantic Parsing
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In: http://aclweb.org/anthology-new/W/W09/W09-1207.pdf
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Using Word Sense Disambiguation for Semantic Role Labeling
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In: http://ir.hit.edu.cn/~car/papers/iucs10.pdf
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Stacking Heterogeneous Joint Models of Chinese POS Tagging and Dependency Parsing
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In: http://ir.hit.edu.cn/~car/papers/coling12-joint.pdf
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A Cascaded Syntactic and Semantic Dependency Parsing System
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In: http://www.cnts.ua.ac.be/conll2008/pdf/23842.pdf
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Stacking Heterogeneous Joint Models of Chinese POS Tagging and Dependency Parsing
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In: http://aclweb.org/anthology/C/C12/C12-1188.pdf
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A Grammar-driven Convolution Tree Kernel for Semantic Role Classification
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In: http://acl.ldc.upenn.edu/p/p07/p07-1026.pdf
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HIT-CIR: An Unsupervised WSD System Based on Domain Most Frequent Sense Estimation
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In: http://aclweb.org/anthology/S/S10/S10-1091.pdf
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Joint Word Alignment and Bilingual Named Entity Recognition Using Dual Decomposition
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In: http://aclweb.org/anthology/P/P13/P13-1106.pdf
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A Grammar-driven Convolution Tree Kernel for Semantic Role Classification
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In: http://www.comp.nus.edu.sg/~tancl/Papers/ACL07/185.pdf
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