TEX: An efficient and effective unsupervised Web information extractor

Abstract

The World Wide Web is an immense information resource. Web information extraction is the task that transforms human friendly Web information into structured information that can be consumed by auto mated business processes. In this article, we propose an unsupervised information extractor that works on two or more web documents generated by the same server side template. It finds and removes shared token sequences amongst these web documents until finding the relevant information that should be extracted from them. The technique is completely unsupervised and does not require maintenance, it allows working on malformed web documents, and does not require the relevant information to be for matted using repetitive patterns. Our complexity analysis reveals that our proposal is computationally tractable and our empirical study on real-world web documents demonstrates that it performs very fast and has a very high precision and recall.