| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560455 |
| ISSN: | 1405-5546 |
| Autors: | Harris, Christopher G1 Srinivasan, Padmini2 |
| Institucions: | 1University of Northern Colorado, Computer Science Department, Colorado. Estados Unidos de América 2University of Iowa, Computer Science Department, Iowa. Estados Unidos de América |
| Any: | 2019 |
| Període: | Jul-Sep |
| Volum: | 23 |
| Número: | 3 |
| Paginació: | 893-904 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Acronyms are commonly used in human language as alternative forms of concepts to increase recognition, to reduce duplicate references to the same concept, and to stress important concepts. There are no standard rules for acronym creation; therefore, both machine-based acronym identification and acronym resolution are highly prone to error. This might be resolved by a human computation approach, which can take advantage of knowledge external to the document collection. Using three text collections with different properties, we compare a machine-based algorithm with a crowdsourcing approach to identify acronyms. We then perform acronym resolution using these two approaches, plus a game-based approach. The crowd and game-based methods outperform the machine algorithm, even when external information is not used. Also, crowd and game formats offered similar performance with a difference in cost. |
| Disciplines | Ciencias de la computación |
| Paraules clau: | Inteligencia artificial |
| Keyword: | Human computation, Crowdsourcing, Acronym identification, Acronym resolution, Gamification, Artificial intelligence |
| Text complet: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
