NNBlocks: A Deep Learning Framework for Computational Linguistics Neural Network Models

被引:0
作者
Caroli, Frederico Tommasi [1 ]
Pereira da Silva, Joao Carlos [1 ]
Freitas, Andre [2 ]
Handschuh, Siegfried [2 ]
机构
[1] Univ Fed Rio de Janeiro, Rio De Janeiro, Brazil
[2] Univ Passau, Passau, Germany
来源
LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | 2016年
关键词
Deep Learning; Artificial Neural Network; Computational Linguistics;
D O I
暂无
中图分类号
H [语言、文字];
学科分类号
05 ;
摘要
Lately, with the success of Deep Learning techniques in some computational linguistics tasks, many researchers want to explore new models for their linguistics applications. These models tend to be very different from what standard Neural Networks look like, limiting the possibility to use standard Neural Networks frameworks. This work presents NNBlocks, a new framework written in Python to build and train Neural Networks that are not constrained by a specific kind of architecture, making it possible to use it in computational linguistics.
引用
收藏
页码:2081 / 2085
页数:5
相关论文
共 11 条
[1]  
[Anonymous], 2010, P PYTH SCI C
[2]  
[Anonymous], P ACL C CIT
[3]  
Goller C, 1996, IEEE IJCNN, P347, DOI 10.1109/ICNN.1996.548916
[4]   A fast learning algorithm for deep belief nets [J].
Hinton, Geoffrey E. ;
Osindero, Simon ;
Teh, Yee-Whye .
NEURAL COMPUTATION, 2006, 18 (07) :1527-1554
[5]  
Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]
[6]  
Le Phong, 2015, SEM, P10, DOI 10.18653/v1/S15-1002
[7]  
Mikolov T., 2012, GOOGL MOUNTN VIEW 2
[8]  
Mikolov T., 2013, ARXIV, DOI [10.48550/arXiv.1301.3781, DOI 10.48550/ARXIV.1301.3781]
[9]  
Socher R, 2012, P 2012 JOINT C EMP M, P1201, DOI DOI 10.1162/153244303322533223
[10]  
Socher R., 2013, P 2013 C EMP METH NA, P1631