Big Text to Knowledge (BText2K)
The work carried out in this WP targets the extraction of knowledge from unstructured text, especially the huge amounts of text (“Big Text”) stored in, or flowing through, large infrastructures like a cloud or the Web. In particular, there is a high interest in the extraction of binary relations between named-entities and relations that characterize entities. This kind of relations are of great relevance, being fundamental for multiple advancements in Natural Language Processing, and also with many interesting applications in different areas of Artificial Intelligence.
Team & Collaborators
- Pais, I. K. Tanoli, M. Albardeiro and J. Cordeiro (2020) Unsupervised Approach to Detect Extreme Sentiments on Social Networks, 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 651-658
- Pais, I. Tanoli; M. Albardeiro; J. Cordeiro (2020) A Lexicon Based Approach to Detect Extreme Sentiments , ICIMP 2020: The Fifteenth International Conference on Internet Monitoring and Protection
- Pais, S., Tanoli, I., Albardeiro, M., & Cordeiro, J.P. (2020). Language-Independent Approaches to Detect Extremism and Collective Radicalisation Online. ICIMP 2020: The Fifteenth International Conference on Internet Monitoring and Protection
- Tanoli, I., & Pais, S. (2020). Modeling Natural Language Policies into Controlled Natural Language: A Twitter Case Study. ICIMP 2020: The Fifteenth International Conference on Internet Monitoring and Protection.
- Moutinho, V.; Brazdil, P., & Cordeiro, J. (2019). Association and Temporality between News and Tweets. Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management – Volume 1: KDIR, pp 500-507.