no code implementations • WS 2019 • Alej Piad-Morffis, ro, Yoan Guit{\'e}rrez, Suilan Estevez-Velarde, Rafael Mu{\~n}oz
This paper presents an annotation model designed to capture a large portion of the semantics of natural language text.
no code implementations • ACL 2019 • Suilan Estevez-Velarde, Yoan Guti{\'e}rrez, Andr{\'e}s Montoyo, Yudivi{\'a}n Almeida-Cruz
The process of extracting knowledge from natural language text poses a complex problem that requires both a combination of machine learning techniques and proper feature selection.
no code implementations • RANLP 2019 • Suilan Estevez-Velarde, Andr{\'e}s Montoyo, Yudivian Almeida-Cruz, Yoan Guti{\'e}rrez, Alej Piad-Morffis, ro, Rafael Mu{\~n}oz
The massive amount of multi-formatted information available on the Web necessitates the design of software systems that leverage this information to obtain knowledge that is valid and useful.
no code implementations • RANLP 2019 • Alej Piad-Morffis, ro, Rafael Mu{\~n}oz, Yoan Guti{\'e}rrez, Yudivian Almeida-Cruz, Suilan Estevez-Velarde, Andr{\'e}s Montoyo
SNNs can be trained to encode explicit semantic knowledge from an arbitrary knowledge base, and can subsequently be combined with other deep learning architectures.
no code implementations • COLING 2020 • Suilan Estevez-Velarde, Yoan Guti{\'e}rrez, Andres Montoyo, Yudivi{\'a}n Almeida Cruz
The system is freely available and includes in-built compatibility with a large number of popular machine learning frameworks, which makes our approach useful for solving practical problems with relative ease and effort.
no code implementations • COLING 2020 • Suilan Estevez-Velarde, Alejandro Piad-Morffis, Yoan Guti{\'e}rrez, Andres Montoyo, Rafael Mu{\~n}oz-Guillena, Yudivi{\'a}n Almeida Cruz
This paper introduces a web demo that showcases the main characteristics of the AutoGOAL framework.
no code implementations • RANLP 2021 • Alejandro Piad-Morffis, Suilan Estevez-Velarde, Ernesto Luis Estevanell-Valladares, Yoan Gutiérrez, Andrés Montoyo, Rafael Muñoz, Yudivián Almeida-Cruz
This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic.
no code implementations • RANLP 2021 • Hian Cañizares-Díaz, Alejandro Piad-Morffis, Suilan Estevez-Velarde, Yoan Gutiérrez, Yudivián Almeida Cruz, Andres Montoyo, Rafael Muñoz-Guillena
Experimental results suggest that the query strategy reduces by between 35% and 40% the number of sentences that must be manually annotated to develop systems able to reach a target F1 score, while the pre-annotation strategy produces an additional 24% reduction in the total annotation time.