Compreensão De Linguagem Natural: Uma solução em português brasileiro
Abstract
The A.D.A (Advanced Distributed Assistant) Project aims to develop an open source personal assistant able to interact with users from an ecosystem of devices through voice commands in brazilian portuguese. In this framework, we intend to interpret commands in natural language and translate them to a compilable formal language. To this work, we did a preliminary study in order to investigate among currently available solutions, based on artificial neural networks capable of capturing different kinds of semantic and syntatic relations, if and how, they could contribute to the development of the solution desired by the project.References
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Grosz, B. J. and Sidner, C. L. (1986). Attention, intentions, and the structure of discourse. Computational linguistics, 12(3):175–204.
Howard, J. and Ruder, S. (2018). Universal language model ne-tuning for text classication. arXiv preprint arXiv:1801.06146.
Luz, F. F. (2019). Deep neural semantic parsing: translating from natural language into SPARQL. PhD thesis, Universidade de São Paulo.
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L. (2018). Deep contextualized word representations. arXiv preprint ar- Xiv:1802.05365.
Roman, N. T. (2001). Estudo de dialogos orientados a tarefa usando a teoria de multiagentes. Master's thesis, Universidade Estadual de Campinas, São Paulo, Brazil.
Souza, F., Nogueira, R., and Lotufo, R. (2019). Portuguese named entity recognition using bert-crf. arXiv preprint arXiv:1909.10649.
Taylor, W. L. (1953). Cloze procedure: a new tool for measuring readability. Journalism Bulletin, pages 415–433.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, ., and Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems, pages 5998–6008. 2https://www.clips.uantwerpen.be/conll2003/ner/
Grosz, B. J. and Sidner, C. L. (1986). Attention, intentions, and the structure of discourse. Computational linguistics, 12(3):175–204.
Howard, J. and Ruder, S. (2018). Universal language model ne-tuning for text classication. arXiv preprint arXiv:1801.06146.
Luz, F. F. (2019). Deep neural semantic parsing: translating from natural language into SPARQL. PhD thesis, Universidade de São Paulo.
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L. (2018). Deep contextualized word representations. arXiv preprint ar- Xiv:1802.05365.
Roman, N. T. (2001). Estudo de dialogos orientados a tarefa usando a teoria de multiagentes. Master's thesis, Universidade Estadual de Campinas, São Paulo, Brazil.
Souza, F., Nogueira, R., and Lotufo, R. (2019). Portuguese named entity recognition using bert-crf. arXiv preprint arXiv:1909.10649.
Taylor, W. L. (1953). Cloze procedure: a new tool for measuring readability. Journalism Bulletin, pages 415–433.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, ., and Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems, pages 5998–6008. 2https://www.clips.uantwerpen.be/conll2003/ner/
Published
2020-08-19
How to Cite
VICENTE NETO, Augusto; DO ESPÍRITO SANTO, Guilherme; GOLDMAN, Alfredo.
Compreensão De Linguagem Natural: Uma solução em português brasileiro. In: REGIONAL SCHOOL OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING, 1. , 2020, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
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p. 5-8.
