Modeling human newspaper readers: The Fuzzy Believer approach

RALF KRESTEL, SABINE BERGLER, RENÉ WITTE
<span title="2012-10-12">2012</span> <i title="Cambridge University Press (CUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ugfrgjxxffftblvvdh5ko6pyum" style="color: black;">Natural Language Engineering</a> </i> &nbsp;
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 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1017/s1351324912000289">doi:10.1017/s1351324912000289</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fr6unjumwzdxjngwae2oje7vke">fatcat:fr6unjumwzdxjngwae2oje7vke</a> </span>
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