A class of neural-network-based transducers for web information extraction
The Web is a huge and still growing information repository that has attracted the attention of many companies. Many such companies rely on information extractors to integrate information that is buried into semi-structured web documents into automatic business processes. Many information extractors...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/143173 |
| Acceso en línea: | https://hdl.handle.net/11441/143173 https://doi.org/10.1016/j.neucom.2013.05.057 |
| Access Level: | acceso abierto |
| Palabra clave: | web wrappers web informationextraction neural networks finite automata machine learning supervised method |
| Sumario: | The Web is a huge and still growing information repository that has attracted the attention of many companies. Many such companies rely on information extractors to integrate information that is buried into semi-structured web documents into automatic business processes. Many information extractors build on extraction rules,which can be hand crafted or learned using supervised or unsupervised techniques. The literature provides a variety of techniques to learn information extraction rules that build on ad hoc machine learning techniques. In this paper, we propose a hybrid approach that explores the use of standard machine-learning techniques to extract web information. We have specifically explored using neural networks; our results show that our proposal out performs three state-of-the-arttechniques in the literature, which opens up quite a new approach to information extraction. |
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