Kobti, Z., and D. Wu (Eds.), Krestel, R., R. Witte, and S. Bergler, "{Creating a Fuzzy Believer to Model Human Newspaper Readers}", Proceedings of the 20th Canadian Conference on Artificial Intelligence (Canadian A.I. 2007), vol. 4509, Montréal, Québec, Canada, Springer, pp. 489–501, May 28–30, 2007.

Krestel, R., and S. Bergler, "{Processing of Beliefs extracted from Reported Speech in Newspaper Articles}", International Conference on Recent Advances in Natural Language Processing (RANLP 2007), Borovets, Bulgaria, September 27–29, 2007.

Krestel, R., R. Witte, and S. Bergler, "{Believe It or Not: Solving the TAC 2009 Textual Entailment Tasks through an Artificial Believer System}", Proceedings of the Second Text Analysis Conference (TAC 2009), Gaithersburg, Maryland, USA, National Institute of Standards and Technology (NIST), November 16–17, 2009.

Krestel, R., S. Bergler, and R. Witte, "{A Belief Revision Approach to Textual Entailment Recognition}", Proceedings of the First Text Analysis Conference (TAC 2008), Gaithersburg, Maryland, USA, National Institute of Standards and Technology (NIST), November 17--19, 2008.

Reported speech in the form of direct and indirect reported speech is an important indicator of evidentiality in traditional newspaper texts, but also increasingly in the new media that rely heavily on citation and quotation of previous postings, as for instance in blogs or newsgroups. We developed an NLP component in form of a GATE resource that can automatically detect and tag reported speech constructs, in particular the source, reporting verb and content. This is intended as a first module for more sophisticated representation and reasoning with attributed information, such as belief reasoning based on nested belief structures.

The growing number of publicly available information sources makes it impossible for individuals to keep track of all the various opinions on one topic. The goal of our fuzzy believer system presented in this paper is to extract and analyze statements of opinion from newspaper articles.

Beliefs are modeled using a fuzzy-theoretic approach applied after NLP-based information extraction. A fuzzy believer models a human agent, deciding what statements to believe or reject based on different, configurable strategies.