Plataforma inteligente para la recuperación, análisis y representación de la información generada por usuarios en Internet

  1. Canales Zaragoza, Lea
  2. Guillén, Antonio
  3. Gutiérrez, Yoan
  4. Gómez, José M.
  5. Llopis, Fernando
Journal:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Year of publication: 2018

Issue: 61

Pages: 127-130

Type: Article

More publications in: Procesamiento del lenguaje natural

Abstract

This project is motivated by the need of defining a platform based on Human Language Technologies capable of intelligently processing textual information, by combining multiple techniques and tools. In addition, the way of displaying the obtained results will be adapted to the users needs from an analytical point of view. The scientific progresses of each technology involved, as well as their combination and integration in a single infrastructure, will contribute to the progress of human language technologies, being in turn of valuable use for the current and future society.

Bibliographic References

  • Cadilhac, A., A. Chisholm, B. Hachey, y S. Kharazmi. 2015. Hugo: Entity-based News Search and Summarisation. En Proceedings of the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval - ESAIR ’15, páginas 51–54.
  • El-Helw, A., M. H. Farid, y I. F. Ilyas. 2012. Just-in-time information extraction using extraction views. En Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD ’12, p´aginas 613–616, New York, NY, USA. ACM.
  • Fernández, J., Y. Gutiérrez, J. M. Gómez, P. Martínez-Barco, A. Montoyo, y R. Muñoz. 2013. Sentiment Analysis of Spanish Tweets Using a Ranking Algorithm and Skipgrams. XXIX Congreso de la Sociedad Española de Procesamiento de Lenguaje Natural (SEPLN 2013), páginas 133–142.
  • Gutiérrez, Y., S. V´Vázquez, y A. Montoyo. 2017. Spreading semantic information by Word Sense Disambiguation. Knowledge-Based Systems, 132:47–61.
  • Iglesias, E. L., A. Seara Vieira, y L. Borrajo. 2013. An HMM-based oversampling technique to improve text classification. Expert Systems with Applications, 40(18):7184–7192.
  • Irfan, R., C. K. King, D. Grages, S. Ewen, S. U. Khan, S. A. Madani, J. Kolodziej, L. Z. Wang, D. Chen, A. Rayes, N. Tziritas, C. Z. Xu, A. Y. Zomaya, A. S. Alzahrani, y H. X. Li. 2015. A survey on text mining in social networks. Knowledge Engineering Review, 30(2):157–170.
  • Li, Y. y K. D. Joshi. 2012. The state of social computing research: A literature review and synthesis using the latent semantic analysis approach. En 18th Americas Conference on Information Systems 2012, AMCIS 2012, volumen 1, páginas 33–40.
  • Moen, H., L. M. Peltonen, J. Heimonen, A. Airola, T. Pahikkala, T. Salakoski, y S. Salanterä. 2016. Comparison of automatic summarisation methods for clinical free text notes. Artificial Intelligence in Medicine, 67:25–37.
  • Ravi, K. y V. Ravi. 2015. A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89:14–46.
  • Zhang, X., J. Zhao, y Y. LeCun. 2015. Character-level convolutional networks for text classification. En Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, NIPS’15, páginas 649–657, Cambridge, MA, USA. MIT Press.