@inproceedings{boros-etal-2018-nlp,
    title = "{NLP}-Cube: End-to-End Raw Text Processing With Neural Networks",
    author = "Boros, Tiberiu  and
      Dumitrescu, Stefan Daniel  and
      Burtica, Ruxandra",
    editor = "Zeman, Daniel  and
      Haji{\v{c}}, Jan",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/K18-2017/",
    doi = "10.18653/v1/K18-2017",
    pages = "171--179",
    abstract = "We introduce NLP-Cube: an end-to-end Natural Language Processing framework, evaluated in CoNLL`s {\textquotedblleft}Multilingual Parsing from Raw Text to Universal Dependencies 2018{\textquotedblright} 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."
}