Applying Neural Networks to English-Chinese Named Entity Transliteration
暂无分享,去创建一个
[1] Jörg Tiedemann,et al. Boosting English-Chinese Machine Transliteration via High Quality Alignment and Multilingual Resources , 2015, NEWS@ACL.
[2] Haizhou Li,et al. Proceedings of the 4th Named Entity Workshop , 2012 .
[3] Jian Su,et al. A Joint Source-Channel Model for Machine Transliteration , 2004, ACL.
[4] Hermann Ney,et al. A Deep Learning Approach to Machine Transliteration , 2009, WMT@EACL.
[5] Grzegorz Kondrak,et al. Applying Many-to-Many Alignments and Hidden Markov Models to Letter-to-Phoneme Conversion , 2007, NAACL.
[6] Wen-Lian Hsu,et al. Cost-benefit Analysis of Two-Stage Conditional Random Fields based English-to-Chinese Machine Transliteration , 2012, NEWS@ACL.
[7] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[8] Philipp Koehn,et al. Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.
[9] Jason Weston,et al. Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..
[10] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[11] Haizhou Li,et al. Whitepaper of NEWS 2010 Shared Task on Transliteration Generation , 2010, NEWS@ACL.
[12] Lemao Liu,et al. Neural Network Transduction Models in Transliteration Generation , 2015, NEWS@ACL.
[13] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..