| Revue: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560379 |
| ISSN: | 1405-5546 |
| Autores: | Ngo, Thi-Lan1 Vu, Tu2 Takeda, Hideaki3 Pham, Son Bao4 Phan, Xuan Hieu4 |
| Instituciones: | 1Thai Nguyen University of Information and Communication Technology, Thai Nguyen. Vietnam 2Hanoi University of Science and Technology, Hanoi. Vietnam 3National Institute of Informatics, Tokyo. Japón 4Vietnam National University, Ho Chi Minh City. Vietnam |
| Año: | 2018 |
| Periodo: | Oct-Dic |
| Volumen: | 22 |
| Número: | 4 |
| Paginación: | 1385-1393 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Suggestion classification for opinion data is defined as identifying a given utterance by suggestion or non-suggestion class. In this paper, we introduce a method called LLMaxent which is the solution for the cross-domain suggestion classification. LLMaxent is a lifelong machine learning approach using maximum entropy (Maxent). In the course of lifelong learning, the drawn knowledge from the past tasks is retained and supported for the future learning. From that, we build a classifier by using labeled data in existed domains for suggestion classification in a new domain. The experimental results show that the proposed novel model can improve the performance of cross-domain suggestion classification. This is one of the preliminary research in lifelong machine learning using Maxent. Its effect is not only for suggestion classification but also for cross-domain text classification in general. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Suggestion mining, Cross-domain suggestion classification, Lifelong learning, Maximum entropy, Artificial intelligence |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
