Skip to main content

Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1057))

Abstract

The increasing availability of digital collections of historical and contemporary literature presents a wealth of possibilities for new research in the humanities. The scale and diversity of such collections however, presents particular challenges in identifying and extracting relevant content. This paper presents Curatr, an online platform for the exploration and curation of literature with machine learning-supported semantic search, designed within the context of digital humanities scholarship. The platform provides a text mining workflow that combines neural word embeddings with expert domain knowledge to enable the generation of thematic lexicons, allowing researches to curate relevant sub-corpora from a large corpus of 18th and 19th century digitised texts.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    British Library Labs: https://www.bl.uk/projects/british-library-labs.

  2. 2.

    http://lucene.apache.org/solr/.

  3. 3.

    Ricorso: Database of Irish writers http://www.ricorso.net.

  4. 4.

    Farjeon features more prominently in histories of Australian literature as he emigrated there.

References

  1. Bailey, E., et al.: CULTURA: supporting enhanced exploration of cultural archives through personalisation. In: the Proceedings of the 2nd International Conference on Humanities, Society and Culture, ICHSC. ICHSC (2012)

    Google Scholar 

  2. Barry, C.L.: User-defined relevance criteria: an exploratory study. J. Am. Soc. Inf. Sci. 45(3), 149–159 (1994)

    Article  Google Scholar 

  3. Bates, M.J.: The Getty end-user online searching project in the humanities: report no. 6: overview and conclusions. Coll. Res. Libr. 57(6), 514–523 (1996)

    Article  Google Scholar 

  4. Camacho-Collados, J., Pilehvar, M.T.: On the role of text preprocessing in neural network architectures: an evaluation study on text categorization and sentiment analysis. arXiv preprint arXiv:1707.01780 (2017)

  5. Chanen, A.: Deep learning for extracting word-level meaning from safety report narratives. In: Integrated Communications Navigation and Surveillance (ICNS), p. 5D2-1. IEEE (2016)

    Google Scholar 

  6. Chiticariu, L., Li, Y., Reiss, F.R.: 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, pp. 827–832 (2013)

    Google Scholar 

  7. Clarke, C.L., et al.: Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 659–666. ACM (2008)

    Google Scholar 

  8. Cohn, S.K.: Pandemics: waves of disease, waves of hate from the plague of athens to aids. Hist. Res. 85(230), 535–555 (2012)

    Article  Google Scholar 

  9. Dempster, J.A.: Thomas Nelson and Sons in the late nineteenth century: a study in motivation. Part 1. Publ. Hist. 13, 41 (1983)

    Google Scholar 

  10. Fast, E., Chen, B., Bernstein, M.S.: Empath: understanding topic signals in large-scale text. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 4647–4657. ACM (2016)

    Google Scholar 

  11. Firth, J.R.: A synopsis of linguistic theory, 1930–1955. In: Studies in Linguistic Analysis (1957)

    Google Scholar 

  12. Flanders, J., Jannidis, F.: The Shape of Data in Digital Humanities: Modeling Texts and Text-based Resources. Routledge, Abingdon (2018)

    Book  Google Scholar 

  13. Frank, A., Bögel, T., Hellwig, O., Reiter, N.: Semantic annotation for the digital humanities. Linguist. Issues Lang. Technol. 7(1), 1–21 (2012)

    Google Scholar 

  14. Hamilton, W.L., Clark, K., Leskovec, J., Jurafsky, D.: Inducing domain-specific sentiment lexicons from unlabeled corpora. In: Proceedings of the EMNLP 2016, vol. 2016, p. 595. NIH Public Access (2016)

    Google Scholar 

  15. Hampson, C., Munnelly, G., Bailey, E., Lawless, S., Conlan, O.: Improving user control and transparency in the digital humanities. In: 2013 International Conference on Culture and Computing (Culture Computing), pp. 196–197. IEEE (2013)

    Google Scholar 

  16. Hinrichs, U., et al.: Trading consequences: a case study of combining text mining and visualization to facilitate document exploration. Digit. Sch. Humanit. 30(suppl\(\_\)1), i50–i75 (2015)

    Google Scholar 

  17. Jackson, H.J.: Marginalia: Readers Writing in Books. Yale University Press, New Haven (2002)

    Google Scholar 

  18. Jockers, M.: Detecting and characterizing national style in the 19th century novel. In: Digital Humanities, Stanford, CA (2011)

    Google Scholar 

  19. Kinealy, C.: This Great Calamity: The Great Irish Famine: The Irish Famine 1845–52. Gill & Macmillan Ltd., Dublin (2006)

    Google Scholar 

  20. Kopaczyk, J.: The Legal Language of Scottish Burghs: Standardization and Lexical Bundles (1380–1560). Oxford University Press, Oxford (2013)

    Book  Google Scholar 

  21. Leavy, S., Pine, E., Keane, M.T.: Industrial memories: exploring the findings of government inquiries with neural word embedding and machine learning. In: Brefeld, U., et al. (eds.) ECML PKDD 2018. LNCS, vol. 11053, pp. 687–690. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10997-4_52

    Chapter  Google Scholar 

  22. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  23. Morash, C.: The Hungry Voice: The Poetry of the Irish Famine. Irish Academic Press, Newbridge (2009)

    Google Scholar 

  24. Mulvey-Roberts, M.: The Handbook of the Gothic. Springer, Heidelberg (2016)

    Google Scholar 

  25. Murray, J.: The social enterprise law market. Md. L. Rev. 75, 541 (2015)

    Google Scholar 

  26. Nelkin, D., Gilman, S.L.: Placing blame for devastating disease. Soc. Res. 55, 361–378 (1988)

    Google Scholar 

  27. Park, D., Kim, S., Lee, J., Choo, J., Diakopoulos, N., Elmqvist, N.: ConceptVector: text visual analytics via interactive lexicon building using word embedding. IEEE Trans. Visual Comput. Graph. 24(1), 361–370 (2018)

    Article  Google Scholar 

  28. Rochelson, M.J.: “They that walk in darkness”: Ghetto tragedies: the uses of Christianity in Israel Zangwill’s fiction. Victorian Lit. Cult. 27(1), 219–233 (1999)

    Article  Google Scholar 

  29. Rochelson, M.J.: A Jew in the Public Arena: The Career of Israel Zangwill. Wayne State University Press, Detroit (2010)

    Google Scholar 

  30. Subramanian, A., Pruthi, D., Jhamtani, H., Berg-Kirkpatrick, T., Hovy, E.: SPINE: sparse interpretable neural embeddings. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  31. Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1555–1565 (2014)

    Google Scholar 

  32. Udelson, J.H.: Dreamer of the Ghetto: The Life and Works of Israel Zangwill. University of Alabama Press, Tuscaloosa (1990)

    Google Scholar 

  33. Van Cranenburgh, A., van Dalen-Oskam, K., van Zundert, J.: Vector space explorations of literary language. Lang. Resour. Eval. (2019)

    Google Scholar 

  34. Vane, O.: Text visualisation tool for exploring digitised historical documents. In: Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 153–158. ACM (2018)

    Google Scholar 

  35. Wohlgenannt, G., Chernyak, E., Ilvovsky, D.: Extracting social networks from literary text with word embedding tools. In: Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities, pp. 18–25 (2016)

    Google Scholar 

  36. Wolfe, J.: Annotations and the collaborative digital library: effects of an aligned annotation interface on student argumentation and reading strategies. Int. J. Comput.-Support. Collab. Learn. 3(2), 141 (2008)

    Article  Google Scholar 

Download references

Acknowledgements

This research was partially supported by Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Susan Leavy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Leavy, S., Meaney, G., Wade, K., Greene, D. (2019). Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds) Metadata and Semantic Research. MTSR 2019. Communications in Computer and Information Science, vol 1057. Springer, Cham. https://doi.org/10.1007/978-3-030-36599-8_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36599-8_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36598-1

  • Online ISBN: 978-3-030-36599-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics