Abstract
In the context of the extraction of the semantic contents important for the effective exploitation of the documents which are now made available by medical information systems, we consider the processing of relations connecting named entities and propose an unsupervised approach to their recognition and labeling. The approach is applied to an Italian data set of medical reports, and interesting results are presented and discussed from a qualitative point of view.
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Notes
- 1.
Freely available at https://code.google.com/archive/p/word2vec/.
- 2.
- 3.
- 4.
- 5.
We used the Italian stop word list provided by Lucene and available at https://lucene.apache.org/core/4_4_0/analyzers-common/org/apache/lucene/analysis/it/ItalianAnalyzer.html.
- 6.
The software is freely available at https://code.google.com/p/word2vec/.
- 7.
Nausea persists with vomiting, hiccups, and general malaise, fasting.
References
Alicante, A., Corazza, A., Isgrò, F., Silvestri, S.: Unsupervised entity and relation extraction from clinical records in Italian. Comput. Biol. Med. (2016)
Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of the International Conference on Learning Representations (ICLR 2013), pp. 1–12 (2013)
Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, New York (2014)
Wei, C., Peng, Y., Leaman, R., Davis, A., Mattingly, C., Li, J., Wiegers, T., Lu, Z.: Overview of the BioCreative V Chemical Disease Relation (CDR) task. In: Proceedings of the Fifth BioCreative Challenge Evaluation Workshop, pp. 154–166 (2015)
Suárez-Paniagua, V., Segura-Bedmar, I., Martínez, P.: Word embedding clustering for disease named entity recognition. In: Proceedings of the Fifth BioCreative Challenge Evaluation Workshop, pp. 299–304 (2015)
Hashimoto, K., Stenetorp, P., Miwa, M., Tsuruoka, Y.: Task-oriented learning of word embeddings for semantic relation classification. In: Proceedings of the 19th Conference on Computational Natural Language Learning, CoNLL 2015, Beijing, China, July 30–31, pp. 268–278 (2015)
Pianta, E., Girardi, C., Zanoli, R.: The TextPro tool suite. In: Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08), Marrakech, Morocco, pp. 28–30 (2008)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems (NIPS), pp. 3111–3119 (2013)
de Hoon, M.J., Imoto, S., Nolan, J., Miyano, S.: Open source clustering software. Bioinformatics 20, 1453–1454 (2004)
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© 2016 Springer International Publishing Switzerland
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Alicante, A., Corazza, A., Isgrò, F., Silvestri, S. (2016). Semantic Cluster Labeling for Medical Relations. In: Chen, YW., Tanaka, S., Howlett, R., Jain, L. (eds) Innovation in Medicine and Healthcare 2016. InMed 2016. Smart Innovation, Systems and Technologies, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-319-39687-3_18
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DOI: https://doi.org/10.1007/978-3-319-39687-3_18
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