| Revista: | Polibits |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000373735 |
| ISSN: | 1870-9044 |
| Autores: | Gu, Yanhui1 Yang, Zhenglu1 Nakano, Miyuki1 Kitsuregawa, Masaru1 |
| Instituciones: | 1University of Tokyo, Institute of Industrial Science, Tokio. Japón |
| Año: | 2012 |
| Periodo: | Jul-Dic |
| Número: | 46 |
| Paginación: | 23-29 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | Similar sentence matching is an essential issue for many applications, such as text summarization, image extraction, social media retrieval, question-answer model, and so on. A number of studies have investigated this issue in recent years. Most of such techniques focus on effectiveness issues but only a few focus on efficiency issues. In this paper, we address both effectiveness and efficiency in the sentence similarity matching. For a given sentence collection, we determine how to effectively and efficiently identify the top-k semantically similar sentences to a query. To achieve this goal, we first study several representative sentence similarity measurement strategies, based on which we deliberately choose the optimal ones through cross-validation and dynamically weight tuning. The experimental evaluation demonstrates the effectiveness of our strategy. Moreover, from the efficiency aspect, we introduce several optimization techniques to improve the performance of the similarity computation. The trade-off between the effectiveness and efficiency is further explored by conducting extensive experiments |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Procesamiento de datos, Comparación de cadenas, Recuperación de información, Procesamiento de lenguaje natural |
| Keyword: | Computer science, Data processing, Information retrieval, Natural language processing, String matching |
| Texto completo: | Texto completo (Ver HTML) |
