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1
Effective bilingual constraints for semisupervised learning of named entity recognizers
In: http://www.cs.stanford.edu/people/mengqiu/publication/aaai13.pdf (2013)
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2
Improving semantic role labeling with word sense
In: http://www.aclweb.org/anthology-new/N/N10/N10-1030.pdf (2010)
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3
Multilingual Dependency-based Syntactic and Semantic Parsing
In: http://ufal.mff.cuni.cz/conll2009-st/papers/15_conll09st.pdf (2009)
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4
Multilingual dependency-based syntactic and semantic parsing
In: http://www.mt-archive.info/CoNLL-2009-Che.pdf (2009)
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5
Using a Hybrid Convolution Tree Kernel for Semantic Role Labeling
In: http://ir.hit.edu.cn/~car/papers/talip.pdf (2008)
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6
Semantic role labeling using a grammar-driven convolution tree kernel. IEEE transactions on audio, speech, and language processing 16(7):1315
In: http://ir.hit.edu.cn/ir_papers/2008/Semantic Role Labeling Using a Grammar-Driven Convolution Tree Kernel.pdf (2008)
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.
Keyword: such as WHO did WHAT to WHOM; WHEN; WHERE; WHY
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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7
Type-Supervised Domain Adaptation for Joint Segmentation and POS-Tagging
In: http://aclweb.org/anthology/E/E14/E14-1062.pdf
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8
Joint Optimization for Chinese POS Tagging and Dependency Parsing
In: http://ir.hit.edu.cn/~car/papers/ieee14.pdf
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9
Micro blogs Oriented Word Segmentation System
In: http://www.aclweb.org/anthology/W/W12/W12-6316.pdf
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10
HIT-IR-WSD: A WSD System for English Lexical Sample Task
In: http://acl.ldc.upenn.edu/w/w07/W07-2034.pdf
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11
Using a Hybrid Convolution Tree Kernel for Semantic Role Labeling
In: http://www.comp.nus.edu.sg/~tancl/Papers/TALIP/TALIP-Che.pdf
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12
Multilingual Dependency-based Syntactic and Semantic Parsing
In: http://aclweb.org/anthology-new/W/W09/W09-1207.pdf
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13
Using Word Sense Disambiguation for Semantic Role Labeling
In: http://ir.hit.edu.cn/~car/papers/iucs10.pdf
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14
Stacking Heterogeneous Joint Models of Chinese POS Tagging and Dependency Parsing
In: http://ir.hit.edu.cn/~car/papers/coling12-joint.pdf
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15
A Cascaded Syntactic and Semantic Dependency Parsing System
In: http://www.cnts.ua.ac.be/conll2008/pdf/23842.pdf
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16
Stacking Heterogeneous Joint Models of Chinese POS Tagging and Dependency Parsing
In: http://aclweb.org/anthology/C/C12/C12-1188.pdf
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17
A Grammar-driven Convolution Tree Kernel for Semantic Role Classification
In: http://acl.ldc.upenn.edu/p/p07/p07-1026.pdf
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18
HIT-CIR: An Unsupervised WSD System Based on Domain Most Frequent Sense Estimation
In: http://aclweb.org/anthology/S/S10/S10-1091.pdf
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19
Joint Word Alignment and Bilingual Named Entity Recognition Using Dual Decomposition
In: http://aclweb.org/anthology/P/P13/P13-1106.pdf
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20
A Grammar-driven Convolution Tree Kernel for Semantic Role Classification
In: http://www.comp.nus.edu.sg/~tancl/Papers/ACL07/185.pdf
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