@article {nle2012,
	title = {Modeling human newspaper readers: The Fuzzy Believer approach},
	journal = {Natural Language Engineering},
	volume = {20},
	number = {02},
	year = {2014},
	month = {April 2014},
	pages = {261{\textendash}288},
	abstract = {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 <i>Fuzzy Believer</i> system presented in this paper is to extract and analyze statements of opinion from newspaper articles. Beliefs are modeled using the fuzzy set theory, applied after Natural Language Processing-based information extraction. The Fuzzy Believer models a human agent, deciding what statements to believe or reject based on a range of configurable strategies.},
	keywords = {Artificial Believer, Fuzzy Believer, Fuzzy NLP, Textual Entailment Recognition},
	doi = {10.1017/S1351324912000289 },
	url = {http://journals.cambridge.org/repo_A91w9lhA},
	attachments = {https://www.semanticsoftware.info/system/files/nle_fuzzy_believer.pdf},
	author = {Ralf Krestel and Sabine Bergler and Ren{\'e} Witte}
}
