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Information Extraction


FASTUS: A Cascaded Finite-State Transducer for Extracting Information from Natural-Language Text.
Hobbs, Appelt, Bear, Israel, Kameyama, Stickel and Tyson.
Roche and Schabes. MIT Press. Cambridge MA. 1996.

Maximum entropy Markov models for information extraction and segmentation.
A. McCallum, D. Freitag, and F. Pereira. 2000.

SRA: Description of the IE-System used for MUC-7.
Chinatsu Aone and Lauren Halverson and Tom Hampton and Mila Ramos-Santacruz.
Proceedings of MUC-7. 1998.
http://www.muc.saic.com

Message Understanding Conference -- 6: A Brief History.
R. Grishman and B. Sundheim.
coling96. Kopenhagen, Denmark, Europe. 1996.

An Information Extraction Core System for Real World German Text Processing.
G. Neumann and R. Backofen and J. Baur and M. Becker and C. Braun.
anlp97. Washington, USA. March 1997.

Unsupervised Discovery of Scenario-Level Patterns for Information Extraction.
R. Yangarber and R. Grishman and P. Tapanainen and S. Huttunen.
Proceedings of the 6th ANLP. Seattle, USA. April 2000.

The Generic Information Extraction System.
J. Hobbs.
muc4.

FASTUS: A Finite State Processor for Information Extraction from Real World Text.
D. Appelt and J. Hobbs and J. Bear and D. Israel and M. Tyson.
ijcai93. Chambery, France. August 1993.

Information Extraction.
J.Cowie and W.Lehnert.
Communications of the ACM. 39 (1). 1996. 51-87.


http://www.lt-world.org/HLT_Survey/ltw-chapter7-3.pdf



The goal of information extraction (IE) is to build systems that find and link relevant information from natural language text ignoring irrelevant information. The information of interest is typically pre-specified in form of uninstantiated frame-like structures also called templates. The templates are domain and task specific. The major task of an IE-system is then the identification of the relevant parts of the text which are used to fill a template's slots.


IE