| Revue: | Computación y Sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000457496 |
| ISSN: | 1405-5546 |
| Autores: | Pandian, Arun1 Mulaffer, Lamana1 Oflazer, Kemal1 AlZeyara, Amna1 |
| Instituciones: | 1Carnegie Mellon University in Qatar, Doha City. Qatar |
| Año: | 2020 |
| Periodo: | Ene-Mar |
| Volumen: | 24 |
| Número: | 1 |
| Paginación: | 5-16 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Aplicado, descriptivo |
| Resumen en inglés | This paper presents a neural network classifier approach to detecting precise within-document(WD) and cross-document(CD) event coreference clusters effectively using only event mention based features. Our approach does not rely on any event argument features such as semantic roles or spatio-temporal arguments and uses no sophisticated clustering approach. Experimental results on the ECB+ dataset show that our simple approach outperforms state-of-the-art methods for both within-document and cross-document event coreference resolution while producing clusters of high precision, which is useful for several downstream tasks |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial, Procesamiento de datos, Programación, Aprendizaje profundo, Clasificación de documentos, Eventos, Semántica, Redes neuronales |
| Keyword: | Artificial intelligence, Data processing, Programming, Deep learning, Document classification, Events, Semantics, Neural networks |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
