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- W2081020307 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 web documents into automatic business processes. Many information extractors build on extraction rules, which can be handcrafted 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 outperforms three state-of-the-art techniques in the literature, which opens up quite a new approach to information extraction." @default.
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- W2081020307 date "2014-07-01" @default.
- W2081020307 modified "2023-10-03" @default.
- W2081020307 title "A class of neural-network-based transducers for web information extraction" @default.
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- W2081020307 doi "https://doi.org/10.1016/j.neucom.2013.05.057" @default.
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