| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560456 |
| ISSN: | 1405-5546 |
| Autors: | Pay, Tayfun1 Lucci, Stephen2 Cox, James L3 |
| Institucions: | 1Graduate Center of New York, Computer Science Department, New York. Estados Unidos de América 2The City College of New York, Computer Science Department, New York. Estados Unidos de América 3Brooklyn College of New York, Computer and Information Science Department, Brooklyn. Estados Unidos de América |
| Any: | 2019 |
| Període: | Jul-Sep |
| Volum: | 23 |
| Número: | 3 |
| Paginació: | 703-710 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | We construct an ensemble method for automatic keyword extraction from single documents. We utilize three different unsupervised automatic keyword extractors in building our ensemble method. These three approaches provide candidate keywords for the ensemble method without using their respective threshold functions. The ensemble method combines these candidate keywords and recomputes their scores after applying pruning heuristics. It then extracts keywords by employing dynamic threshold functions. We analyze the performance of our ensemble method by using all parts of the Inspect data set. Our ensemble method achieved a better overall performance when compared to the automatic keyword extractors that were used in its development as well as to some recent automatic keyword extraction methods. |
| Disciplines | Ciencias de la computación |
| Paraules clau: | Inteligencia artificial |
| Keyword: | Data mining, Text mining, Text analysis, Ensemble methods, Artificial intelligence |
| Text complet: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
