Abstract
The fast increasing amount of articles published in the biomedical field is creating difficulties in the way this wealth of information can be efficiently exploited by researchers. As a way of overcoming these limitations and potentiating a more efficient use of the literature, we propose an approach for structuring the results of a literature search based on the latent semantic information extracted from a corpus. Moreover, we show how the results of the Latent Semantic Analysis method can be adapted so as to evidence differences between results of different searches. We also propose different visualization techniques that can be applied to explore these results. Used in combination, these techniques could empower users with tools for literature guided knowledge exploration and discovery.
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References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science 41, 391–407 (1990)
Jahiruddin, Abulaish, M., Dey, L.: A concept-driven biomedical knowledge extraction and visualization framework for conceptualization of text corpora. Journal of Biomedical Informatics 43, 1020–1035 (2010)
Kim, J.J., Rebholz-Schuhmann, D.: Categorization of services for seeking information in biomedical literature: a typology for improvement of practice. Briefings in Bioinformatics 9(6), 452–465 (2008)
Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Processes 25(2-3), 259–284 (1998)
Lu, Z.: PubMed and beyond: a survey of web tools for searching biomedical literature. Database 2011, baq036 (2011)
Řehůřek, R., Sojka, P.: Software Framework for Topic Modelling with Large Corpora. In: Proceedings of LREC 2010 Workshop New Challenges for NLP Frameworks, pp. 46–50. LREC (2010)
Shatkay, H.: Hairpins in bookstacks: information retrieval from biomedical text. Briefings in Bioinformatics 6(3), 222–238 (2005)
UMLS Metathesaurus Fact Sheet, http://www.nlm.nih.gov/pubs/factsheets/umlsmeta.html
Van Deun, K., Heiser, W.J., Delbeke, L.: Multidimensional unfolding by nonmetric multi-dimensional scaling of Spearman distances in the extended permutation polytope. Multivariate Behavioral Research 42(1), 103–132 (2007)
Zheng, H.-T., Borchert, C., Jiang, Y.: A knowledge-driven approach to biomedical document conceptualization. Artificial Intelligence in Medicine 49, 67–78 (2010)
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© 2013 Springer International Publishing Switzerland
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Matos, S., Araújo, H., Oliveiras, J.L. (2013). Structuring and Exploring the Biomedical Literature Using Latent Semantics. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-00551-5_72
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DOI: https://doi.org/10.1007/978-3-319-00551-5_72
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00550-8
Online ISBN: 978-3-319-00551-5
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