Abstract
It is relatively easy, albeit time-consuming, for a person to find and select news stories that meet subjective judgments of relevance and interest to a community.NewsFinder is an AI program that automates the steps involved in this task, from crawling the web to publishing the results.NewsFinder incorporates a learning program whose judgment of interestingness of stories can be trained by feedback from readers.Preliminary testing confirms the feasibility of automating the service to write AI in the News for the AAAI.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
5 star rating system for Pmwiki, http://www.pmwiki.org/wiki/Cookbook/StarRater
Buchanan, B.G., Glick, J., Smith, R.G.: The AAAI Video Archive. AI Magazine 29(1), 91–94 (2008)
Burges, J.C.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines, Software (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Gupta, S., Kaiser, G., Neistadt, D., Grimm, P.: DOM-based Content Extraction of HTML Documents. In: Proceedings of the 12th International World Wide Web Conference(WWW2003), Budapest, Hungary (2003)
Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22 (2004)
Loper, E., Bird, S.: NLTK: The Natural Language Toolkit. In: Proceedings of the ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics. Association for Computational Linguistics, Philadelphia (2002), http://www.nltk.org/
Manning, C., et al.: Intro. to Information Retrieval. Cambridge University Press, Cambridge (2008)
Melville, P., Sindhwani, V.: Recommender System. In: Sammut, C., Webb, G. (eds.) Encyclopedia of Machine Learning, Springer, Heidelberg (2010)
Song, R., Liu, H., Wen, J.-R., Ma, W.-Y.: Learning Block Importance Models for Web Pages. In: Proceedings of the 13th International World Wide Web Conference (WWW 2004), New York (2004)
Salton, G., Gerard, Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 513–523 (1988), doi:10.1016/0306-4573(88)90021-0
Zhang, S., Wang, W., Ford, J., Makedon, F.: Learning from incomplete ratings using non-negative matrix factorization. In: Proc. of the 6th SIAM Conference on Data Mining (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dong, L., Smith, R.G., Buchanan, B.G. (2011). Automating the Selection of Storiesfor AI in the News . In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-21822-4_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21821-7
Online ISBN: 978-3-642-21822-4
eBook Packages: Computer ScienceComputer Science (R0)