Presentation | 2004-06-21 Kernel-based Discriminative Learning Algorithms for Labeling Structured Data Hisashi KASHIMA, Yuta TSUBOI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We introduce a new perceptron-based discriminative learning algorithm for labeling structural data such as sequences, trees and graphs. Since it is fully kernelized and employs the pointwise label prediction, large features including arbitrary number of hidden variables can be incorporated with polynomial time complexity. This is contrasted with existing labelers that can handle only features of a small number of hidden variables such as Maximum Entropy Markov Models and Conditional Random Fields. We also introduce several kernel functions for labeling sequences, trees and graphs and the efficient algorithms for them. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Kernel Methods / Perceptron / Marginalized Kernel / Named Entity Recognition / Information Extraction |
Paper # | AI2004-3 |
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Committee | AI |
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Conference Date | 2004/6/14(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Kernel-based Discriminative Learning Algorithms for Labeling Structured Data |
Sub Title (in English) | |
Keyword(1) | Kernel Methods |
Keyword(2) | Perceptron |
Keyword(3) | Marginalized Kernel |
Keyword(4) | Named Entity Recognition |
Keyword(5) | Information Extraction |
1st Author's Name | Hisashi KASHIMA |
1st Author's Affiliation | IBM Tokyo Research Laboratory() |
2nd Author's Name | Yuta TSUBOI |
2nd Author's Affiliation | IBM Tokyo Research Laboratory |
Date | 2004-06-21 |
Paper # | AI2004-3 |
Volume (vol) | vol.104 |
Number (no) | 133 |
Page | pp.pp.- |
#Pages | 6 |
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