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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/K18-2017.pdf
- Code
- adobe/NLP-Cube
- Data
- Universal Dependencies