The Distiller Framework: Current State and Future Challenges

  • Marco Basaldella
  • Giuseppe Serra
  • Carlo Tasso
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 806)


In 2015, we introduced a novel knowledge extraction framework called the Distiller Framework, with the goal of offering the research community a flexible, multilingual information extraction framework [3]. Two years later, the project has significantly evolved, by supporting more languages and many machine learning algorithms. In this paper we present the current design of the framework and some of its applications.


Information extraction Keyphrase extraction Named entity recognition 


  1. 1.
    Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015).
  2. 2.
    Basaldella, M., Chiaradia, G., Tasso, C.: Evaluating anaphora and coreference resolution to improve automatic keyphrase extraction. In: COLING 2016, 26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, Osaka, Japan, 11–16 December 2016, pp. 804–814 (2016)Google Scholar
  3. 3.
    Basaldella, M., De Nart, D., Tasso, C.: Introducing distiller: a unifying framework for knowledge extraction. In: Proceedings of 1st AI*IA Workshop on Intelligent Techniques At Libraries and Archives co-located with XIV Conference of the Italian Association for Artificial Intelligence (AI*IA 2015). Associazione Italiana per l’Intelligenza Artificiale (2015)Google Scholar
  4. 4.
    Basaldella, M., Furrer, L., Colic, N., Ellendorff, T., Tasso, C., Rinaldi, F.: Using a hybrid approach for entity recognition in the biomedical domain. In: Proceedings of the 7th International Symposium on Semantic Mining in Biomedicine, SMBM 2016, Potsdam, Germany, 4–5 August 2016, pp. 11–19 (2016)Google Scholar
  5. 5.
    Basaldella, M., Helmy, M., Antolli, E., Popescu, M.H., Serra, G., Tasso, C.: Exploiting and evaluating a supervised, multilanguage keyphrase extraction pipeline for under-resourced languages. In: Recent Advances in Natural Language Processing 2017 (RANLP 2017), Varna (Bulgaria), 4–6 September 2017Google Scholar
  6. 6.
    Bojar, O., Chatterjee, R., Federmann, C., Graham, Y., Haddow, B., Huck, M., Jimeno Yepes, A., Koehn, P., Logacheva, V., Monz, C., Negri, M., Neveol, A., Neves, M., Popel, M., Post, M., Rubino, R., Scarton, C., Specia, L., Turchi, M., Verspoor, K., Zampieri, M.: Findings of the 2016 conference on machine translation. In: Proceedings of the First Conference on Machine Translation, pp. 131–198. Association for Computational Linguistics, Berlin, August 2016Google Scholar
  7. 7.
    Bojar, O., Chatterjee, R., Federmann, C., Haddow, B., Huck, M., Hokamp, C., Koehn, P., Logacheva, V., Monz, C., Negri, M., Post, M., Scarton, C., Specia, L., Turchi, M.: Findings of the 2015 workshop on statistical machine translation. In: Proceedings of the Tenth Workshop on Statistical Machine Translation, pp. 1–46. Association for Computational Linguistics, Lisbon, September 2015Google Scholar
  8. 8.
    Boudin, F.: pke: an open source python-based keyphrase extraction toolkit. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations. pp. 69–73. The COLING 2016 Organizing Committee, Osaka, December 2016Google Scholar
  9. 9.
    Bougouin, A., Boudin, F., Daille, B.: Topicrank: graph-based topic ranking for keyphrase extraction. In: Sixth International Joint Conference on Natural Language Processing, IJCNLP 2013, Nagoya, Japan, 14–18 October 2013, pp. 543–551 (2013)Google Scholar
  10. 10.
    De Nart, D., Tasso, C.: A domain independent double layered approach to keyphrase generation. In: WEBIST 2014 - Proceedings of the 10th International Conference on Web Information Systems and Technologies, pp. 305–312. SciTePress (2014)Google Scholar
  11. 11.
    Degl’Innocenti, D., De Nart, D., Tasso, C.: A new multi-lingual knowledge-base approach to keyphrase extraction for the Italian language. In: Proceedings of the 6th International Conference on Knowledge Discovery and Information Retrieval, pp. 78–85. SciTePress (2014)Google Scholar
  12. 12.
    Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns: Elements of Reusable Object-oriented Software. Addison-Wesley Longman Publishing Co., Inc., Boston (1995)zbMATHGoogle Scholar
  13. 13.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016).
  14. 14.
    Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations, pp. 55–60 (2014)Google Scholar
  15. 15.
    Medelyan, O., Frank, E., Witten, I.H.: Human-competitive tagging using automatic keyphrase extraction. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, vol. 3, pp. 1318–1327. Association for Computational Linguistics, Stroudsburg (2009)Google Scholar
  16. 16.
    Nguyen, T.D., Luong, M.: WINGNUS: keyphrase extraction utilizing document logical structure. In: Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval@ACL 2010, Uppsala University, Uppsala, Sweden, 15–16 July 2010, pp. 166–169 (2010).
  17. 17.
    Okazaki, N.: Crfsuite: a fast implementation of conditional random fields (crfs) (2007).
  18. 18.
    Porter, M.F.: An Algorithm for suffix stripping. In: Readings in Information Retrieval, pp. 313–316. Morgan Kaufmann Publishers Inc., San Francisco (1997)Google Scholar
  19. 19.
    Pudota, N., Dattolo, A., Baruzzo, A., Ferrara, F., Tasso, C.: Automatic keyphrase extraction and ontology mining for content-based tag recommendation. Int. J. Intell. Syst. 25(12), 1158–1186 (2010)CrossRefzbMATHGoogle Scholar
  20. 20.
    Rinaldi, F.: The ontogene system: an advanced information extraction application for biological literature. EMBnet.journal 18(B) (2012)Google Scholar
  21. 21.
    Rose, S., Engel, D., Cramer, N., Cowley, W.: Automatic keyword extraction from individual documents. In: Text Mining, pp. 1–20 (2010)Google Scholar
  22. 22.
    Russell, I., Markov, Z.: An introduction to the weka data mining system (abstract only). In: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, Seattle, WA, USA, 8–11 March 2017, p. 742 (2017)Google Scholar
  23. 23.
    dos Santos, C., Guimarães, V.: Boosting named entity recognition with neural character embeddings. In: Proceedings of the Fifth Named Entity Workshop, pp. 25–33. Association for Computational Linguistics, Beijing, July 2015Google Scholar
  24. 24.
    Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., Nevill-Manning, C.G.: Kea: practical automatic keyphrase extraction. In: Proceedings of the Fourth ACM Conference on Digital Libraries, pp. 254–255. ACM (1999)Google Scholar
  25. 25.
    Zhang, Q., Wang, Y., Gong, Y., Huang, X.: Keyphrase extraction using deep recurrent neural networks on Twitter. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Laboratorio di Intelligenza ArtificialeUniversità degli Studi di UdineUdineItaly

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