Article
A class of neural-network-based transducers for web information extraction
Author/s | Sleiman, Hassan A.
Corchuelo Gil, Rafael |
Department | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Publication Date | 2013-05 |
Deposit Date | 2023-03-06 |
Published in |
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Abstract | 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 ... 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. |
Funding agencies | Ministerio de Educación y Ciencia (MEC). España Junta de Andalucía Ministerio de Ciencia e Innovación (MICIN). España Ministerio de Economía, Industria y Competitividad Ministerio de Economía y Competitividad (MINECO). España |
Project ID. | TIN2007-64119
P07-TIC-2602 P08-TIC- 4100 TIN2008-04718-E TIN2010-21744 TIN2010-09809-E TIN2010-10811-E TIN2010-09988-E TIN2011-15497-E |
Citation | Sleiman, H.A. y Corchuelo Gil, R. (2013). A class of neural-network-based transducers for web information extraction. Neurocomputing, 135 (5), 61-68. https://doi.org/10.1016/j.neucom.2013.05.057. |
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