| Revista: | Computación y sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000411065 |
| ISSN: | 1405-5546 |
| Autors: | Huang, Jin-Xia1 Lee, Kyung Soon2 Choi, Key-Sun3 Kim, Young-Kil1 |
| Institucions: | 1Electronics and Telecommunications Research Institute, Automatic Speech Translation and Language Intelligence Research Department, Daejeon. Corea del Sur 2Chonbuk National University, Division of Computer Science and Engineering, Jeonju. Corea del Sur 3Korea Advanced Institute of Science and Technology, School of Computing, Daejeon. Corea del Sur |
| Any: | 2016 |
| Període: | Jul-Sep |
| Volum: | 20 |
| Número: | 3 |
| Paginació: | 467-476 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | A feature based relation classification approach is presented in this paper. We aimed to exact relation candidates from Wikipedia texts. A probabilistic and a semantic relatedness features are employed with other linguistic information for the purpose. The experiments show that, relation classification using the proposed relatedness features with surface information like word and part-of-speech tags is competitive with or even outperforms the one of using deep syntactic information. Meanwhile, an approach is proposed to distinguish reliable relation candidates from others, so that these reliable results can be accepted for knowledge building without human verification. The experiments show that, with the relation classification approach presented in this paper, more than 40% of the classification results are reliable, which means, at least 40% of the human and time costs can be saved in practice |
| Disciplines | Ciencias de la computación, Literatura y lingüística |
| Paraules clau: | Procesamiento de datos, Lingüística aplicada, Lingüística computacional, Análisis de textos, Extracción de información, Ontología |
| Keyword: | Computer science, Literature and linguistics, Data processing, Applied linguistics, Computing linguistics, Text analysis, Information extraction, Ontology |
| Text complet: | Texto completo (Ver HTML) |
