TEG - A hybrid approach to information extraction

Ronen Feldman*, Benjamin Rosenfeld, Moshe Fresko

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

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 labour 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 system does not contain any purely linguistic components, such as PoS tagger or shallow parser, but allows to using external linguistic components if necessary. We demonstrate the performance of the system on several named entity extraction and relation extraction tasks. 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 amounts of training data. We also demonstrate the robustness of our system under conditions of poor training-data quality.

Original languageAmerican English
Pages (from-to)1-18
Number of pages18
JournalKnowledge and Information Systems
Volume9
Issue number1
DOIs
StatePublished - Jan 2006
Externally publishedYes

Keywords

  • Hidden Markov models
  • Hybrid approaches
  • Information extraction
  • Rule bases systems
  • Stochastic context free grammars
  • Text mining

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