Hybrid semantic tagging for information extraction

Ronen Feldman, Benjamin Rosenfeld, Moshe Fresko, Brian D. Davison

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


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.

Original languageEnglish
Title of host publication14th International World Wide Web Conference, WWW2005
Number of pages2
StatePublished - 2005
Event14th International World Wide Web Conference, WWW2005 - Chiba, Japan
Duration: 10 May 200514 May 2005

Publication series

Name14th International World Wide Web Conference, WWW2005


Conference14th International World Wide Web Conference, WWW2005


  • HMM
  • Information extraction
  • Rules based systems
  • Semantic web
  • Text mining


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