NLP-Cube: End-to-End Raw Text Processing With Neural Networks

Tiberiu Boros, Stefan Daniel Dumitrescu, Ruxandra Burtica


Abstract
We introduce NLP-Cube: an end-to-end Natural Language Processing framework, evaluated in CoNLL’s “Multilingual Parsing from Raw Text to Universal Dependencies 2018” Shared Task. It performs sentence splitting, tokenization, compound word expansion, lemmatization, tagging and parsing. Based entirely on recurrent neural networks, written in Python, this ready-to-use open source system is freely available on GitHub. For each task we describe and discuss its specific network architecture, closing with an overview on the results obtained in the competition.
Anthology ID:
K18-2017
Volume:
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Month:
October
Year:
2018
Address:
Brussels, Belgium
Editors:
Daniel Zeman, Jan Hajič
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
171–179
Language:
URL:
https://aclanthology.org/K18-2017
DOI:
10.18653/v1/K18-2017
Bibkey:
Cite (ACL):
Tiberiu Boros, Stefan Daniel Dumitrescu, and Ruxandra Burtica. 2018. NLP-Cube: End-to-End Raw Text Processing With Neural Networks. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 171–179, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
NLP-Cube: End-to-End Raw Text Processing With Neural Networks (Boros et al., CoNLL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/K18-2017.pdf
Code
 adobe/NLP-Cube
Data
Universal Dependencies