References
- Blei, D.M., Ng, A.Y., & Jordan, M.I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
- Coulet, A., Shah, N.H., Garten, Y., Musen, M., & Altman, R.B. (2010). Using text to build semantic networks for pharmacogenomics. Journal of Biomedical Informatics, 43(6), 1009–1019.
- Davi, A., Haughton, D., Nasr, N., Shah, G., Skaletsky, M., & Spack, R. (2005). A review of two text-mining packages: SAS textmining and wordstat. The American Statistician, 59(1), 89–103.
- Feldman, R., & Sanger, J. (2007). The text mining handbook: Advanced approach in analyzing unstructured data. Cambridge: Cambridge University Press.
- Jojic, N., Perina, A., & Murino, V. (2010). Structural epitome: A way to summarize one's visual experience. Advances in Neural Information Processing Systems, 23, 1027--1035..
- Kao, A., & Poteet, S.R. (2007). Natural language processing and text mining. London: Springer.
- Kaufman, L., & Rousseeuw, P.J. 1990. Finding groups in data. New York, NY: Wiley.
- Kuo, C.-J., Ling, M.H.T., Lin, K.-T., & Hsu, C.-N. (2009). BIOADI: A machine learning approach to identifying abbreviations and definitions in biological literature. BMC Bioinformatics, 10(Suppl 15), S7.
- Lee, S., Lee, S., Seol, H., & Park, Y. (2008). Using patent information for designing new product and technology: Keyword based technology roadmapping. R&D Management, 38, 169–188.
- Luther, S., Berndt, D., Finch, D., Richardson, M., Hickling, E., & Hickam, D. (2011). Using statistical text mining to supplement the development of an ontology. Journal of Biomedical Informatics, 44, S86–S93.
- Perina, A., Jojic, N., Bicego, M., & Truski, A. (2013). Documents as multiple overlapping windows into grids of counts. In C. Burges (Ed.), Proceedings of the 2013 Conference on Neural Information Processing Systems (pp. 10–18). Tahoe, NV.
- Pison, G., Struyf, A., & Rousseeuw, P.J. (1999). Displaying a clustering with CLUSPLOT. Computational Statistics & Data Analysis, 30(1999), 381–392.
- Porter, M.F. (1980). An algorithm for suffix stripping. Program, 14(3), 130–137.
- Rencher, A.C. (2002). Methods in multivariate analysis (2nd ed.). Hoboken, NJ: John Wiley & Sons.
- Robertson, S.E., & Walker, S. (1994). Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. In W. Bruce Croft & C. J. van Rijsbergen (Eds.), Proceedings ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 232–241). New York, NY: Springer-Verlag.
- Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24(5), 513–523.
- Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge: Cambridge University Press.
- Shulman, L. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14.
- Stankov, S., Rosic, M., Zitko, B., & Grubisic, A. (2008). TEx-Sys model for building intelligent tutoring systems. Computers & Education, 51, 1017–1036.
- Strehl, A., Ghosh, J., & Mooney, R. (2000). Impact of similarity measures on web-page clustering. Austin, TX: Workshop on Artificial Intelligence for Web Search (AAAI 2000).
- Wan, X. (2007). A novel document similarity measure based on earth movers distance. Information Sciences, 177(18), 3718–3730.