Skip to main content Accessibility help
×
Home
Hostname: page-component-558cb97cc8-9njm9 Total loading time: 0.451 Render date: 2022-10-07T12:54:29.051Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": false, "useSa": true } hasContentIssue true

Modeling human newspaper readers: The Fuzzy Believer approach

Published online by Cambridge University Press:  12 October 2012

RALF KRESTEL
Affiliation:
Department of Computer ScienceUniversity of California, Irvine, CA, USA e-mail: krestel@uci.edu
SABINE BERGLER
Affiliation:
Department of Computer Science and Software EngineeringConcordia University, Montréal, Canada e-mail: bergler@cse.concordia.ca, rwitte@cse.concordia.ca
RENÉ WITTE
Affiliation:
Department of Computer Science and Software EngineeringConcordia University, Montréal, Canada e-mail: bergler@cse.concordia.ca, rwitte@cse.concordia.ca

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 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.

Type
Articles
Copyright
Copyright © Cambridge University Press 2012 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Balahur, A., Steinberger, R., der Goot, E. V., Pouliquen, B., and Kabadjov, M. A. 2009. Opinion mining on newspaper quotations. In Proceedings of the Web Intelligence/IAT Workshops’2009, pp. 523–6. Milan, Italy.Google Scholar
Ballim, A., and Wilks, Y. 1991. Artificial Believers: The Ascription of Belief. Mahwah, NJ, USA: Lawrence Erlbaum Associates.Google Scholar
Ballim, A., Wilks, Y., and Barnden, J. A. 1991. Belief ascription, metaphor, and intensional identification. Cognitive Science 15: 133–71.CrossRefGoogle Scholar
Bar-Haim, R., Dagan, I., Dolan, B., Ferro, L., Giampiccolo, D., Magnini, B., and Szpektor, I. 2006. The second PASCAL recognising textual entailment challenge. In Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment, pp. 19. Venice, Italy.Google Scholar
Bentivogli, L., Dagan, I., Dang, H. T., Giampiccolo, D., and Magnini, B. 2009. The fifth PASCAL recognizing textual entailment challenge. In Proceedings of TAC’2009, pp. 115. Gaithersburg, Maryland, USA.Google Scholar
Bergler, S. 1992. The Evidential Analysis of Reported Speech. PhD thesis, Brandeis University, Massachusetts, USA.Google Scholar
Bergler, S. 1995a. From lexical semantics to text analysis. In Saint-Dizier, P. and Viegas, E. (eds.), Computational Lexical Semantics, pp. 98124. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Bergler, S. 1995b. Generative lexicon principles for machine translation: a case for meta-lexical structure. Journal of Machine Translation 9 (3): 4155.Google Scholar
Bergler, S. 2005. Conveying attitude with reported speech. In Shanahan, J. C., Qu, Y., and Wiebe, J. (eds.), Computing Attitude and Affect in Text: Theory and Applications, pp. 1123. New York, USA: Springer-Verlag.Google Scholar
Bergler, S., Doandes, M., Gerard, C., and Witte, R. 2004. Attributions. In Shanahan, Y. Qu, J., and Wiebe, J. (eds.), Exploring Attitude and Affect in Text: Theories and Applications, pp. 1619. Technical Report SS-04-07. Stanford, CA, USA: AAAI Press.Google Scholar
Briscoe, T., Carroll, J., and Watson, R. 2006. The second release of the RASP system. In Proceedings of the COLING/ACL on Interactive Presentation Sessions. (COLING-ACL '06), pp. 7780. Stroudsburg, PA, USA: Association for Computational Linguistics.CrossRefGoogle Scholar
Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V., Aswani, N., Roberts, I., Gorrell, G., Funk, A., Roberts, A., Damljanovic, D., Heitz, T., Greenwood, M. A., Saggion, H., Petrak, J., Li, Y., and Peters, W. 2011. Text Processing with GATE (Version 6). South Yorkshire, UK: Department of Computer Science, University of Sheffield.Google Scholar
Cunningham, H., Maynard, D., and Tablan, V. 2000 (November). JAPE: A Java Annotation Patterns Engine, 2nd ed. Research Memorandum CS–00–10. South Yorkshire, UK: Department of Computer Science, University of Sheffield.Google Scholar
Dagan, I., Glickman, O., and Magnini, B. 2005. The PASCAL recognising textual entailment challenge. In Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment.Google Scholar
Dang, H. T., and Owczarzak, K. 2008. Overview of the TAC 2008 update summarization task. In Proceedings of the Text Analysis Conference 2008.Google Scholar
Das, A. S., Datar, M., Garg, A., and Rajaram, S. 2007. Google news personalization: scalable online collaborative filtering. Proceedings of the 16th International Conference on World Wide Web (WWW '07), pp. 271–80. New York, NY, USA: ACM.CrossRefGoogle Scholar
Diab, M. T., Levin, L., Mitamura, T., Rambow, O., Prabhakaran, V., and Guo, W. 2009. Committed belief annotation and tagging. In Proceedings of the Third Linguistic Annotation Workshop at ACL-IJCNLP09, Singapore, pp. 6873. Stroudsburg, PA, USA: Association for Computational Linguistics.CrossRefGoogle Scholar
Doandes, M. 2003. Profiling For Belief Acquisition From Reported Speech. Master's thesis, Concordia University, Montreal, Quebec, Canada.Google Scholar
Dolan, B., Brockett, C., and Quirk, C. 2005 (March). Mirosoft Research Paraphrase Corpus. http://research.microsoft.com/en-us/downloads/607d14d9-20cd-47e3-85bc-a2f65cd28042/ (Accessed 3 Oct 2012).Google Scholar
Doms, A., and Schroeder, M. 2005. GoPubMed: exploring PubMed with the gene ontology. Nucleic Acids Research 33 (suppl 2): W7836.CrossRefGoogle ScholarPubMed
Evans, D. K., Klavans, J. L., and McKeown, K. R. 2004. Columbia Newsblaster: multilingual news summarization on the web. In Demonstration Papers at the Proceedings of HLT-NAACL 2004(HLT-NAACL–Demonstrations '04), pp. 14. Stroudsburg, PA, USA: Association for Computational Linguistics.Google Scholar
Fellbaum, C. (ed). 1998. WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press.Google Scholar
Gaizauskas, R., Hepple, M., Saggion, H., Greenwood, M. A., and Humphreys, K. 2005. SUPPLE: a practical parser for natural language engineering applications. In Proceedings of the Ninth International Workshop on Parsing Technology (Parsing '05), pp. 200–1. Stroudsburg, PA, USA: Association for Computational Linguistics.CrossRefGoogle Scholar
Gamon, M., Aue, A., Corston-Oliver, S., and Ringger, E. K. 2005. Pulse: mining customer opinions from free text. In Famili, A. F., Kok, J. N., Peña, J. M., Siebes, A., and Feelders, A. J. (eds.), Advances in Intelligent Data Analysis VI, Proceedings of the 6th International Symposium on Intelligent Data Analysis, IDA 2005, Madrid, Spain, September 8–10, pp. 121–32, LNCS vol. 3646. New York, USA: Springer.Google Scholar
Gerard, C. 2000. Modelling Readers of News Articles Using Nested Beliefs. Master's thesis, Concordia University, Montreal, Quebec, Canada.Google Scholar
Giampiccolo, D., Dang, H. T., Magnini, B., Dagan, I., Cabrio, E., and Dolan, B. 2008. The fourth PASCAL recognizing textual entailment challenge. In Proceedings of TAC’2008.Google Scholar
Jurafsky, D., and Martin, J. H. 2008. Speech and Language Processing, 2nd ed.Upper Saddle River, New Jersey, USA: Pearson-Prentice Hall.Google Scholar
Kim, S.-M., and Hovy, E. 2006. Identifying and analyzing judgment opinions. In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, pp. 200–7. New York City, NY, USA: Association for Computational Linguistics.Google Scholar
Kim, J. D., Pyysalo, S., Ohta, T., Bossy, R., Nguyen, N., and Tsujii, J. 2011a. Overview of BioNLP shared task 2011. In Proceedings of BioNLP Shared Task 2011, a Workshop at ACL HLT 2011.Google Scholar
Kim, J.-D., Pyysalo, S., Ohta, T., Bossy, R., Nguyen, N., and Tsujii, J. 2011b. Overview of BioNLP shared task 2011. In Proceedings of BioNLP Shared Task 2011 Workshop.Google Scholar
Klebanov, B. B. 2006. Measuring semantic relatedness using people and WordNet. In Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers, pp. 1316. New York City, USA: Association for Computational Linguistics.Google Scholar
Klein, D., and Manning, C. D. 2003a. Accurate unlexicalized parsing. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics (ACL '03), pp. 423–30. Morristown, NJ, USA: Association for Computational Linguistics.Google Scholar
Klein, D., and Manning, C. D. 2003b. Fast exact inference with a factored model for natural language parsing. In Advances in Neural Information Processing Systems, vol. 15. Cambridge, MA: MIT Press.Google Scholar
Krestel, R., Bergler, S., and Witte, R. 2008a. A belief revision approach to textual entailment recognition. In Proceedings of Text Analysis Conference (TAC 2008). Gaithersburg, MD, USA: National Institute of Standards and Technology (NIST).Google Scholar
Krestel, R., Bergler, S., and Witte, R. 2008b. Minding the source: automatic tagging of reported speech in newspaper articles. In Proceedings of the Sixth International Language Resources and Evaluation (LREC 2008), pp. 2823–28. Paris, France: European Language Resources Association (ELRA).Google Scholar
Krestel, R., Bergler, S., and Witte, R. 2009. Believe it or not: solving the TAC 2009 textual entailment tasks through an Artificial Believer System. In Proceedings of Text Analysis Conference (TAC 2009). Gaithersburg, MD, USA: National Institute of Standards and Technology (NIST).Google Scholar
Krestel, R., Witte, R., and Bergler, S. 2007a. Creating a fuzzy believer to model human newspaper readers. In Kobti, Z. and Wu, D. (eds), Proceedings of the 20th Canadian Conference on Artificial Intelligence (Canadian A.I. 2007), pp. 489501, LNAI vol. 4509. Montréal, Québec, Canada: Springer.Google Scholar
Krestel, R., Witte, R., and Bergler, S. 2007b. Processing of beliefs extracted from reported speech in newspaper articles. In Proceedings of Recent Advances in Natural Language Processing (RANLP-2007), September 27–29.Google Scholar
Krestel, R., Witte, R., and Bergler, S. 2010. Predicate-Argument EXtractor (PAX). In Proceedings of the First Workshop on New Challenges for NLP Frameworks Co-located with LREC 2010, pp. 51–4. Paris, France: European Language Resources Association (ELRA).Google Scholar
Lin, D. 1998. Dependency based evaluation of MINIPAR. In Proceedings of the Workshop on the Evaluation of Parsing Systems, First International Conference on Language Resources and Evaluation.Google Scholar
MacCartney, B., Grenager, T., de Marneffe, M.-C., Cer, D., and Manning, C. D. 2006. Learning to recognize features of valid textual entailments. In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, pp. 41–8. New York City, NY, USA: Association for Computational Linguistics.Google Scholar
McKeown, K. R., Barzilay, R., Evans, D., Hatzivassiloglou, V., Klavans, J. L., Nenkova, A., Sable, C., Schiffman, B., and Sigelman, S. 2002. Tracking and summarizing news on a daily basis with Columbia's Newsblaster. In Proceedings of the Second International Conference on Human Language Technology Research (HLT '02), pp. 280–5. San Francisco, CA, USA: Morgan Kaufmann.CrossRefGoogle Scholar
McKeown, K., Passonneau, R. J., Elson, D. K., Nenkova, A., and Hirschberg, J. 2005. Do summaries help? In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '05), pp. 210–17. New York, NY, USA: ACM.Google Scholar
Merlo, P., and Ferrer, E. E. 2006. The notion of argument in prepositional phrase attachment. Computational Linguistics 32 (3): 341–78.CrossRefGoogle Scholar
Nelson, S. J., Johnston, W. D., and Humphreys, B. L. 2001. Relationships in medical subject headings. In Bean, Carol A., and Green, Rebecca (eds.), Relationships in the Organization of Knowledge, pp. 171–84. New York: Kluwer.CrossRefGoogle Scholar
Pang, B., and Lee, L. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2 (12). doi:10.1561/1500000011.CrossRefGoogle Scholar
Pouliquen, B., Steinberger, R., and Best, C. 2007. Automatic detection of quotations in multilingual news. In Proceedings of Recent Advances in Natural Language Processing (RANLP-2007), September 27–29.Google Scholar
Prabhakaran, V., Rambow, O., and Diab, M. T. 2010. Automatic committed belief tagging. In Huang, C.-R. and Jurafsky, D. (eds.), 23rd International Conference on Computational Linguistics, Posters Volume (COLING 2010), August 23–27, Beijing, China, pp. 1014–22. Beijing, China: Chinese Information Processing Society of China.Google Scholar
Quirk, R. 1985. A Comprehensive Grammar of the English Language. London, UK: Longman.Google Scholar
Ruppenhofer, J., Sporleder, C., and Shirokov, F. 2010. Speaker attribution in cabinet protocols. In Proceedings of the International Conference on Language Resources and Evaluation (LREC 2010), May 17–23, Valletta, Malta. Paris, France: European Language Resources Association.Google Scholar
Snow, R., Vanderwende, L., and Menezes, A. 2006. Effectively using syntax for recognizing false entailment. In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, pp. 3340. New York City, USA: Association for Computational Linguistics.Google Scholar
Somasundaran, S., Namata, G., Getoor, L., and Wiebe, J. 2009. Opinion graphs for polarity and discourse classification. In Proceedings of TextGraphs-4: Graph-based Methods for Natural Language Processing, Singapore, August 7, 2009.Google Scholar
Witte, R. 2002. Fuzzy belief revision. In Proceedings of the 9th International Workshop on Non-Monotonic Reasoning (NMR’02), April 19–21, pp. 311–20.Google Scholar
Witte, R., and Bergler, S. 2003. Fuzzy coreference resolution for summarization. In Proceedings of 2003 International Symposium on Reference Resolution and Its Applications to Question Answering and Summarization (ARQAS), June 23–24, pp. 4350. Venice, Italy: Università Ca’ Foscari.Google Scholar
Zadeh, L. A. 1965. Fuzzy sets. Information and Control 8 (3): 338–53.CrossRefGoogle Scholar

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Modeling human newspaper readers: The Fuzzy Believer approach
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Modeling human newspaper readers: The Fuzzy Believer approach
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Modeling human newspaper readers: The Fuzzy Believer approach
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *