A class of neural-network-based transducers for web information extraction

作者：Sleiman Hassan A^*; Corchuelo Rafael

来源：Neurocomputing, 2014, 135: 61-68.

DOI：10.1016/j.neucom.2013.05.057

• web wrappers
• web information extraction
• neural networks
• finite automata
• machine learning
• supervised method

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.

• 出版日期2014-7-5