@inproceedings{rodrigues-etal-2020-deepnlpf,
    title = "{D}eep{NLPF}: A Framework for Integrating Third Party {NLP} Tools",
    author = "Rodrigues, Francisco  and
      Lima, Rinaldo  and
      Domingues, William  and
      Fidalgo, Robson  and
      Chifu, Adrian  and
      Espinasse, Bernard  and
      Fournier, S{\'e}bastien",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2020.lrec-1.895",
    pages = "7244--7251",
    abstract = "Natural Language Processing (NLP) of textual data is usually broken down into a sequence of several subtasks, where the output of one the subtasks becomes the input to the following one, which constitutes an NLP pipeline. Many third-party NLP tools are currently available, each performing distinct NLP subtasks. However, it is difficult to integrate several NLP toolkits into a pipeline due to many problems, including different input/output representations or formats, distinct programming languages, and tokenization issues. This paper presents DeepNLPF, a framework that enables easy integration of third-party NLP tools, allowing the user to preprocess natural language texts at lexical, syntactic, and semantic levels. The proposed framework also provides an API for complete pipeline customization including the definition of input/output formats, integration plugin management, transparent ultiprocessing execution strategies, corpus-level statistics, and database persistence. Furthermore, the DeepNLPF user-friendly GUI allows its use even by a non-expert NLP user. We conducted runtime performance analysis showing that DeepNLPF not only easily integrates existent NLP toolkits but also reduces significant runtime processing compared to executing the same NLP pipeline in a sequential manner.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}

Natural Language Processing (NLP) of textual data is usually broken down into a sequence of several subtasks, where the output of one the subtasks becomes the input to the following one, which constitutes an NLP pipeline. Many third-party NLP tools are currently available, each performing distinct NLP subtasks. However, it is difficult to integrate several NLP toolkits into a pipeline due to many problems, including different input/output representations or formats, distinct programming languages, and tokenization issues. This paper presents DeepNLPF, a framework that enables easy integration of third-party NLP tools, allowing the user to preprocess natural language texts at lexical, syntactic, and semantic levels. The proposed framework also provides an API for complete pipeline customization including the definition of input/output formats, integration plugin management, transparent ultiprocessing execution strategies, corpus-level statistics, and database persistence. Furthermore, the DeepNLPF user-friendly GUI allows its use even by a non-expert NLP user. We conducted runtime performance analysis showing that DeepNLPF not only easily integrates existent NLP toolkits but also reduces significant runtime processing compared to executing the same NLP pipeline in a sequential manner.

@inproceedings{pereira2019using,
  title={Using assistive robotics for aphasia rehabilitation},
  author={Pereira, Jayr and de Melo, Mariana and Franco, Natalia and Rodrigues, Francisco and Coelho, Alexsandro and Fidalgo, Robson},
  booktitle={2019 Latin American Robotics Symposium (LARS), 2019 Brazilian Symposium on Robotics (SBR) and 2019 Workshop on Robotics in Education (WRE)},
  pages={387--392},
  year={2019},
  organization={IEEE},
  doi={10.1109/LARS-SBR-WRE48964.2019.00074},
  url={https://ieeexplore.ieee.org/document/9018569}
}

@inproceedings{rodrigues2015jean,
  author    = {Francisco de Assis de Souza Rodrigues and Elias Alves de Carvalho Neto and Tamires Alencar Souza},
  title     = {JEAN – Uma Interface Cérebro-Máquina de Baixo Custo Ajudando Deficientes a Mover Coisas no Mundo},
  booktitle = {Anais da VII Semana de Iniciação Científica},
  year      = {2015},
  month     = {October},
  url       = {http://www.fjn.edu.br/iniciacaocientifica/anais-vii-semana/wp-content/uploads/2016/01/Anais2.pdf}
}

@phdthesis{de2013titulo,
  title={T{\'I}TULO: DISPOSITIVO DE APOIO A DEFICIENTES VISUAIS-DAVI T{\'I}TULO},
  author={DE SOUZA RODRIGUES, JONATAS LEVI DOS},
  year={2013},
  school={FACULDADE DE CI{\^E}NCIAS APLICADAS DOUTOR LE{\~A}O SAMPAIO}
}