| Revue: | Computación y Sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000457361 |
| ISSN: | 1405-5546 |
| Autores: | Choi, Won Gyu1 Lee, Kyung Soon1 |
| Instituciones: | 1Jeonbuk National University, Division of Computer Science and Engineering, Jeonju. Corea del Sur |
| Año: | 2019 |
| Periodo: | Oct-Dic |
| Volumen: | 23 |
| Número: | 4 |
| Paginación: | 1523-1531 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Aplicado, descriptivo |
| Resumen en inglés | The importance of social media such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly recognized. During crisis situations, rapid and effective response actions by emergency services are critical to assure the safety of the public. In this paper, we propose a conceptual representation for crisis-related tweet classification. In order to classify a stream of tweets related to the incident, the crisis-related terms in each tweet are represented as conceptual entities such as event entities, category indicator entities, information type entities, URL entities, and user entities. For tweet classification, we have compared support vector machines and deep learning model which combines class activation mapping with one-shot learning in convolutional neural networks. Experimental results on TREC 2018 Incident Streams test collection show significant improvement over the baseline system |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial, Procesamiento de datos, Redes, Conceptualización, Twitter, Crisis, Red neuronal convolucional, Mapeo, Máquinas de vectores de soporte |
| Keyword: | Artificial intelligence, Data processing, Networks, Conceptualization, Twitter, Crisis, Convolutional neural network, Mapping, Support vector machines |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
