| Revue: | Computación y sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000423293 |
| ISSN: | 1405-5546 |
| Autores: | Gopalan, Sindhuja1 Devi, Sobha Lalitha1 |
| Instituciones: | 1Anna University, AU-KBC Research Centre, Chennai, Tamil Nadu. India |
| Año: | 2017 |
| Periodo: | Oct-Dic |
| Volumen: | 21 |
| Número: | 4 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Aplicado, descriptivo |
| Resumen en inglés | The objective of the present work is to automatically extract the cause and effect from discourse analyzed biomedical corpus. Cause-effect is defined as a relation established between two events, where first event acts as the cause of second event and the second event is the effect of first event. Any causative constructions need three components, a causal marker, cause and effect. In this study, we consider the automatic extraction of cause and effect realized by explicit discourse connective markers. We evaluated our system using BIONLP/NLPBA 2004 shared task test data and obtained encouraging results |
| Disciplinas: | Ciencias de la computación, Literatura y lingüística |
| Palabras clave: | Lingüística aplicada, Análisis del discurso, Causa-efecto, Discurso conectivo, Entidad causal, Reconocimiento de entidades |
| Keyword: | Applied linguistics, Discourse analysis, Cause-effect, Discourse connective, Causal entity, Named entity recognition |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
