Mapeamento do Perfil das Mulheres Brasileiras em Processamento de Linguagem Natural
Resumo
Conhecer o perfil das mulheres brasileiras que atuam em Processamento de Linguagem Natural (PLN) é um importante passo para o desenvolvimento de políticas e programas que visem aumentar a inclusão e a diversidade nessa área. Este é o primeiro trabalho realizado no Brasil com este fim. A partir de dados coletados via consulta pública, Lattes e Linkedin, notou-se que o perfil é de uma formação em computação ou linguística, atuando em empresas ou universidades, mas com pouca diversidade étnica e aparente dificuldade em conciliar vida profissional e maternidade. Analisando mais especificamente o grupo “Brasileiras em PLN” constatou-se uma expressiva capacidade de publicação e orientação, mas ainda uma baixa colaboração entre nossas integrantes.
Referências
Ahmad, S. (2017). Family or future in the academy? Review of Educational Research, 87(1):204–239.
Alam, A. (2022). Psychological, sociocultural, and biological elucidations for gender gap in stem education: A call for translation of research into evidence-based interventions. In Alam, A.(2022). Psychological, Sociocultural, and Biological Elucidations for Gender Gap in STEM Education: A Call for Translation of Research into Evidence-Based Interventions. Proceedings of the 2nd International Conference on Sustainability and Equity (ICSE-2021). Atlantis Highlights in Social S.
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023). A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023.
Cordeiro, D., Rocha, A., Cassiano, K. K., and da Silva, N. (2020). Representativeness of women in postgraduate programs in computer science in Brazil. In Anais do XIV Women in Information Technology, pages 110–119, Porto Alegre, RS, Brasil. SBC.
Dong, C., Li, Y., Gong, H., Chen, M., Li, J., Shen, Y., and Yang, M. (2022). A survey of natural language generation. ACM Computing Surveys, 55(8):1–38.
Freitas, C. (2022). Linguística Computacional. Parábola, São Paulo.
Hango, D. (2013). Gender Differences in Science, Technology, Engineering, Mathematics and Computer Science (STEM) Programs at University. Insights on Canadian Society.
Hill, C., Corbett, C., and St. Rose, A. (2010). Why so few? Women in science, technology, engineering, and mathematics. American Association of University Women., Washington, D.C.
Klocker, N. and Drozdzewski, D. (2012). Commentary: Career progress relative to opportunity: how many papers is a baby ’worth’?
Lorens, A. L., Botelho, J., Moura, A. F., Duarte, B., and Moro, M. (2020). Participação feminina em comitês de programa de simpósios da computação. In Anais do XIV Women in Information Technology, pages 90–99, Porto Alegre, RS, Brasil. SBC.
Metz, I. (2005). Advancing the careers of women with children. Career Development International.
Mohammad, S. M. (2020). Gender gap in natural language processing research: Disparities in authorship and citations. In Proceedings of the 58th Annual Meeting of ACL, pages 7860–7870, Online. Association for Computational Linguistics.
Richter, A., Yamamoto, J., and Frachtenberg, E. (2023). Why are there so few women in computer systems research? Computer, 56(2):101–105.
Wang, M.-T. and Degol, J. L. (2017). Gender gap in science, technology, engineering, and mathematics (stem): Current knowledge, implications for practice, policy, and future directions. Educational psychology review, 29:119–140.
Wang, Y., Wang, Y., Liu, J., and Liu, Z. (2020). A comprehensive survey of grammar error correction. arXiv preprint arXiv:2005.06600.
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.
Zhang, L., Wang, S., and Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4):e1253.
Alam, A. (2022). Psychological, sociocultural, and biological elucidations for gender gap in stem education: A call for translation of research into evidence-based interventions. In Alam, A.(2022). Psychological, Sociocultural, and Biological Elucidations for Gender Gap in STEM Education: A Call for Translation of Research into Evidence-Based Interventions. Proceedings of the 2nd International Conference on Sustainability and Equity (ICSE-2021). Atlantis Highlights in Social S.
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023). A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023.
Cordeiro, D., Rocha, A., Cassiano, K. K., and da Silva, N. (2020). Representativeness of women in postgraduate programs in computer science in Brazil. In Anais do XIV Women in Information Technology, pages 110–119, Porto Alegre, RS, Brasil. SBC.
Dong, C., Li, Y., Gong, H., Chen, M., Li, J., Shen, Y., and Yang, M. (2022). A survey of natural language generation. ACM Computing Surveys, 55(8):1–38.
Freitas, C. (2022). Linguística Computacional. Parábola, São Paulo.
Hango, D. (2013). Gender Differences in Science, Technology, Engineering, Mathematics and Computer Science (STEM) Programs at University. Insights on Canadian Society.
Hill, C., Corbett, C., and St. Rose, A. (2010). Why so few? Women in science, technology, engineering, and mathematics. American Association of University Women., Washington, D.C.
Klocker, N. and Drozdzewski, D. (2012). Commentary: Career progress relative to opportunity: how many papers is a baby ’worth’?
Lorens, A. L., Botelho, J., Moura, A. F., Duarte, B., and Moro, M. (2020). Participação feminina em comitês de programa de simpósios da computação. In Anais do XIV Women in Information Technology, pages 90–99, Porto Alegre, RS, Brasil. SBC.
Metz, I. (2005). Advancing the careers of women with children. Career Development International.
Mohammad, S. M. (2020). Gender gap in natural language processing research: Disparities in authorship and citations. In Proceedings of the 58th Annual Meeting of ACL, pages 7860–7870, Online. Association for Computational Linguistics.
Richter, A., Yamamoto, J., and Frachtenberg, E. (2023). Why are there so few women in computer systems research? Computer, 56(2):101–105.
Wang, M.-T. and Degol, J. L. (2017). Gender gap in science, technology, engineering, and mathematics (stem): Current knowledge, implications for practice, policy, and future directions. Educational psychology review, 29:119–140.
Wang, Y., Wang, Y., Liu, J., and Liu, Z. (2020). A comprehensive survey of grammar error correction. arXiv preprint arXiv:2005.06600.
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.
Zhang, L., Wang, S., and Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4):e1253.
Publicado
06/08/2023
Como Citar
CASELI, Helena; AMORIM, Evelin; SCHNEIDER, Elisa Terumi Rubel; FREITAS, Leidiana Iza Andrade; RODRIGUES, Jéssica; NUNES, Maria das Graças V..
Mapeamento do Perfil das Mulheres Brasileiras em Processamento de Linguagem Natural. In: WOMEN IN INFORMATION TECHNOLOGY (WIT), 17. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 182-193.
ISSN 2763-8626.
DOI: https://doi.org/10.5753/wit.2023.229504.
