Fuzzy Set Theory-Based Belief Processing for Natural Language Texts

Introduction

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 artificial believer system we present in this paper is to extract and analyze opinionated statements from newspaper articles.

Beliefs are modeled with 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.

Fuzzy Believer Poster at AAAI 2007

Reference

Ralf Krestel, René Witte, and Sabine Bergler. Fuzzy Set Theory-Based Belief Processing for Natural Language Texts. Twenty-Second AAAI Conference on Artificial Intelligence (AAAI 2007), Student Abstract Program/Poster Presentation, pp.1878-1879, July 22-26, 2007, Vancouver, British Columbia, Canada. AAAI Press.

Bibtex entry (also for download):

@InProceedings{KWB_AAAI2007,
  author = 	 {Ralf Krestel and Ren{\'e} Witte and Sabine Bergler},
  title = 	 {{Fuzzy Set Theory-Based Belief Processing 
                  for Natural Language Texts}},
  booktitle =	 {Proceedings of the Twenty-Second AAAI Conference 
                  on Artificial Intelligence (AAAI)},
  pages =	 {1878--1879},
  year =	 {2007},
  address =	 {Vancouver, British Columbia, Canada},
  month =	 {July 22--26},
  publisher =	 {AAAI Press},
  isbn =         {978-1-57735-323-2}
}

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