An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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The semantic web is expected to have an impact at least as big as that of the existing HTML based web, if not greater. However, the challenge lays in creating this semantic web and in converting existing web information into the semantic paradigm. One of the core technologies that can help in migration process is automatic markup, the semantic markup of content, providing the semantic tags to describe the raw content. This paper describes a hybrid statistical and knowledge-based information extraction model, able to extract entities and relations at the sentence level. The model attempts to retain and improve the high accuracy levels of knowledge-based systems while drastically reducing the amount of manual labor by relying on statistics drawn from a training corpus. The implementation of the model, called TEG (Trainable Extraction Grammar), can be adapted to any IE domain by writing a suitable set of rules in a SCFG (Stochastic Context Free Grammar) based extraction language, and training them using an annotated corpus. The experiments show that our hybrid approach outperforms both purely statistical and purely knowledge-based systems, while requiring orders of magnitude less manual rule writing and smaller amount of training data. We also demonstrate the robustness of our system under conditions of poor training data quality. This makes the system very suitable for converting legacy web pages to semantic web pages.