Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Comparative Study of Information Extraction Strategies
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Segmental semi-markov models and applications to sequence analysis
Segmental semi-markov models and applications to sequence analysis
Using the structure of Web sites for automatic segmentation of tables
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
OLERA: Semisupervised Web-Data Extraction with Visual Support
IEEE Intelligent Systems
Web data extraction based on partial tree alignment
WWW '05 Proceedings of the 14th international conference on World Wide Web
Extracting web data using instance-based learning
WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
NET – a system for extracting web data from flat and nested data records
WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
Automatic extraction rules generation based on XPath pattern learning
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
Automatic web information extraction based on rules
WISE'11 Proceedings of the 12th international conference on Web information system engineering
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Automated techniques can help to extract information from the Web. A new semi-automatic approach based on the maximum entropy segmental Markov model, therefore, is proposed to extract structured data from Web pages. It is motivated by two ideas: modeling sequences embedding structured data instead of their context to reduce the number of training Web pages and preventing the generation of too specific or too general models from the training data. The experimental results show that this approach has better performance than Stalker when only one training Web page is provided.