Skip to main content

Advertisement

Log in

Current trends in applied machine intelligence

  • HAUPTBEITRAG
  • CURRENT TRENDS IN APPLIED MACHINE INTELLIGENCE
  • Published:
Informatik Spektrum Aims and scope

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

  1. Academy of Medical Sciences (2015) Stratified, personalised or P4 medicine: a new direction for placing the patient at the centre of healthcare and health education (technical report). Academy of Medical Sciences. May 2015. https://acmedsci.ac.uk/viewFile/564091e072d41.pdf. Accessed 23 Sept 2018

  2. Bense H, Bodrow W (1995) Objektorientierte und regelbasierte Wissensverarbeitung. Spektrum Akademischer Verlag, Heidelberg

    MATH  Google Scholar 

  3. Bense H, Gernhardt B, Haase P, Hoppe T, Hemmje M, Humm B, Paschke A, Schade U, Schäfermeier R, Schmidt M, Siegel M, Vogel T, Wenning R (2016) Emerging trends in corporate semantic web – selected results of the 2016 Dagstuhl workshop on corporate semantic web. Informatik-Spektrum 39(6):474–480

    Google Scholar 

  4. Bird S, Klein E, Loper E (2009) Natural language processing with Python. O’Reilly Media, Sebastopol, CA

    MATH  Google Scholar 

  5. Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Trans Assoc Comput Linguist 5:135–146, http://aclweb.org/anthology/Q17-1010, last access: 24.9.2018

    Article  Google Scholar 

  6. Bond F, Foster R (2013) Linking and extending an open multilingual wordnet. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, pp 1352–1362

  7. Busse J, Humm B, Lübbert C, Moelter F, Reibold A, Rewald M, Schlüter V, Seiler B, Tegtmeier E, Zeh T (2015) Actually, what does “Ontology” mean? A term coined by philosophy in the light of different scientific disciplines. J Comput Informat Technol (CIT) 23(1):29–41, https://doi.org/10.2498/cit.1002508

    Article  Google Scholar 

  8. Chiticariu L, Li Y, Reiss FR (2013) Rule-based information extraction is dead! Long live rule-based information extraction systems! in: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, 18–21 October 2013. Association for Computational Linguistics, Stroudsburg, pp 827–832

  9. Ege B, Humm B, Reibold A (eds) (2015) Corporate Semantic Web – Wie Anwendungen in Unternehmen Nutzen stiften. Springer, Heidelberg (in German)

  10. Fellbaum C (ed) (1998) WordNet: An electronic lexical database. MIT Press, Cambridge

  11. Harris-Ferrante K (2017) To the Point: Leveraging AI for Success in Digital Insurance. In: Presentation Gartner Symposium ITXPO, Nov. 5–7, 2017, Barcelona, Spain

  12. Hoppe T, Humm B, Schade U, Heuss T, Hemmje M, Vogel T, Gernhardt B (2015) Corporate semantic web – applications, technology, methodology. Informatik-Spektrum 39(1):57–63, https://doi.org/10.1007/s00287-015-0939-0

    Article  Google Scholar 

  13. Hoppe T, Humm BG, Reibold A (eds) (2018) Semantic Applications – Methodology, Technology, Corporate Use. Springer, Berlin

  14. Humm BG, Walsh P (2018) Personalised clinical decision support for cancer care. In: Hoppe T, Humm BG, Reibold A (eds) Semantic Applications – Methodology, Technology, Corporate Use. Springer, Berlin, pp 125–143

    Chapter  Google Scholar 

  15. Kirrane S, Wenning R (2018) Compliance using metadata. In: Hoppe T, Humm BG, Reibold A (eds) Semantic Applications – Methodology, Technology, Corporate Use. Springer, Berlin, pp 31–45

    Google Scholar 

  16. Manning CD (2015) Computational linguistics and deep learning. Comput Linguist 41(4):701–707

    Article  MathSciNet  Google Scholar 

  17. McDonald A, Cranor L (2008) The cost of reading privacy policies. I/S J Law Policy Inf Soc. 2008 Privacy Year in Review issue. http://aleecia.com/authors-drafts/readingPolicyCost-AV.pdf, last access: 20.11.2018

  18. Mikolov T et al (2013) Efficient estimation of word representations in vector space. https://en.wikipedia.org/wiki/ArXiv https://arxiv.org/abs/1301.3781, last access: 24.9.2018

  19. Murugan R (2015) Movement towards personalised medicine in the ICU. Lancet Respir Med 3(1):10–12

    Article  Google Scholar 

  20. Pennington J, Socher R, Manning CD (2014) GloVe: Global vectors for word representation. in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 25–29 October 2014, Doha, pp 1532–1543. https://nlp.stanford.edu/pubs/glove.pdf, last access: 23.9.2018

  21. Robertson S (2004) Understanding inverse document frequency: On theoretical arguments for IDF. J Doc 60(5):503–520, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.438.2284&rep=rep1&type=pdf, last access: 28.8.2018

    Article  Google Scholar 

  22. Ruder S (2018) Repository to track the progress in Natural Language Processing (NLP). https://github.com/sebastianruder/NLP-progress, last access: 23.9.2018

  23. Tractica.com (2017) Natural language processing market to reach $22.3 billion by 2025, August 21. https://www.tractica.com/newsroom/press-releases/natural-language-processing-market-to-reach-22-3-billion-by-2025, last access: 23.9.2018

  24. Young T, Hazarika D, Poria S, Cambria E (2017) Recent trends in deep learning based natural language processing. IEEE Comput Intell Mag 13(3):55–75

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bernhard G. Humm.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Humm, B., Bense, H., Classen, M. et al. Current trends in applied machine intelligence. Informatik Spektrum 42, 28–37 (2019). https://doi.org/10.1007/s00287-018-01127-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00287-018-01127-0

Navigation