In this article, we describe how we are using text mining solutions to enhance the production of systematic reviews. The aims of this collaborative project are the development of a text mining framework to support systematic reviews and the provision of a service exemplar serving as a test bed for deriving requirements for the development of more generally applicable text mining tools and services.
Bollegala, D., Okazaki, N., & Ishizuka, M. ( 2006). A bottom-up approach to sentence ordering for multi-document summarization. Proceedings from the 21st International Conference on Computational Linguistics, 385-392.
2.
Carroll, J., Evans, R., & Klein, E. ( 2005). Supporting text mining for e-science: The challenges for grid-enabled natural language processing. Proceedings from the UK e-Science All Hands Meeting, Nottingham, UK.
3.
Chalmers, I. ( 2003). Trying to do more good than harm in policy and practice: The role of rigorous, transparent, up-to-date evaluations. Annals of the American Academy of Political and Social Science, 589, 22-40.
4.
Dumais, S., Platt, J., Heckerman, D., & Sahami, M. ( 1998). Inductive learning algorithms and representations for text categorization. In G. Gardarin (Ed.), Information and knowledge management (pp. 148-155). Bethesda, MD : ACM Press.
5.
Frantzi, K., Ananiadou, S., & Mima, H. ( 2000). Automatic recognition of multi-word terms: The C-value/NCvalue method. International Journal on Digital Libraries, 3, 115-130.
6.
Hartswood, M., Procter, R., Rouncefield, M., Slack, R., & Voss, A. ( 2008). Co-realisation: Evolving IT artefacts by design. In M. Ackerman, T. Erickson, C. Halverson, and W. Kellog (Eds.), Resources, co-evolution and artifacts. Springer.
7.
Hey, T., & Trefethen, A. ( 2003). e-Science and its implications. Philosophical Transactions . Series A, Mathematical, Physical, and Engineering Sciences , 361, 1809-1825.
8.
Hull, D., Pettifer, S.R., & Kell, D.B. ( 2008). Defrosting the digital library: Bibliographic tools for the next generation web. PLoS Computational Biology, 4(10), e1000204.
9.
Jirotka, M., & Goguen, J. (Eds.). (1994). Requirements engineering: Social and technical issues. London: Academic Press.
10.
Joachims, T. ( 1998). Text categorization with support vector machines: Learning with many relevant features. Lecture Notes in Computer Science , 1398, 137-142.
11.
Lin, C.Y., & Hovy, E. ( 2002). From single to multi-document summarization: A prototype system and its evaluation. Proceedings from the 40th Annual Meeting on Association for Computational Linguistics, Philadelphia, PA, 457-464.
12.
Okazaki, N., & Ananiadou, S. ( 2006). A term recognition approach to acronym recognition. Proceedings from the 21st International Conference on Computational Linguistics, Sydney, Australia, 643-650.
13.
Okazaki, N., Matsuo, Y., & Ishizuka, M. ( 2004). TISS: An integrated summarization system for TSC-3. Proceedings from NTCIR-4, Tokyo.
14.
Osiński, S., & Weiss, D. (Eds.). (2005). Carrot2: Design of a flexible and efficient web information retrieval framework. Lecture notes in computer science, 3528, 439-444.
15.
Sebastiani, F. ( 2002). Machine learning in automated text categorization. Computing Surveys, 34, 1-47.
16.
Sasaki, Y., Rea, B., & Ananiadou, S. ( 2007). Multi-topic aspects in clinical text classification. Proceedings from the IEEE International Conference on BioInformation and BioMedicine.
17.
Tsuruoka, Y., McNaught, J., & Ananiadou, S. ( 2008). Normalizing biomedical terms by minimizing ambiguity and variability. BMC Bioinformatics, 9(Suppl. 3), S2.
