@inproceedings{caroli-etal-2016-nnblocks,
title = "{NNB}locks: A Deep Learning Framework for Computational Linguistics Neural Network Models",
author = "Caroli, Frederico Tommasi and
Freitas, Andr{\'e} and
da Silva, Jo{\~a}o Carlos Pereira and
Handschuh, Siegfried",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1330",
pages = "2081--2085",
abstract = "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.",
}
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%0 Conference Proceedings
%T NNBlocks: A Deep Learning Framework for Computational Linguistics Neural Network Models
%A Caroli, Frederico Tommasi
%A Freitas, André
%A da Silva, João Carlos Pereira
%A Handschuh, Siegfried
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F caroli-etal-2016-nnblocks
%X 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.
%U https://aclanthology.org/L16-1330
%P 2081-2085
Markdown (Informal)
[NNBlocks: A Deep Learning Framework for Computational Linguistics Neural Network Models](https://aclanthology.org/L16-1330) (Caroli et al., LREC 2016)
ACL