Accelerated training of maximum margin markov models for sequence labeling: A case study of NP chunking
Refereed conference paper presented and published in conference proceedings


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AbstractWe present the first known empirical results on sequence labeling based on maximum margin Markov networks (M3N), which incorporate both kernel methods to efficiently deal with high-dimensional feature spaces, and probabilistic graphical models to capture correlations in structured data. We provide an efficient algorithm, the stochastic gradient descent (SGD), to speedup the training procedure of M3N. Using official dataset for noun phrase (NP) chunking as a case study, the resulting optimizer converges to the same quality of solution over an order of magnitude faster than the structured sequential minimal optimization (structured SMO). Our model compares favorably with current state-of-the-art sequence labeling approaches. More importantly, our model can be easily applied to other sequence labeling tasks.
All Author(s) ListYu X., Lam W.
Name of Conference23rd International Conference on Computational Linguistics, Coling 2010
Start Date of Conference23/08/2010
End Date of Conference27/08/2010
Place of ConferenceBeijing
Country/Region of ConferenceChina
Detailed descriptionorganized by The Chinese Information Processing Society of China (CIPS),
Year2010
Month12
Day1
Volume Number2
Pages1408 - 1416
LanguagesEnglish-United Kingdom

Last updated on 2020-29-03 at 23:49