Text Adaptation Using Formal Concept Analysis

  • Valmi Dufour-Lussier
  • Jean Lieber
  • Emmanuel Nauer
  • Yannick Toussaint
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6176)


This paper addresses the issue of adapting cases represented by plain text with the help of formal concept analysis and natural language processing technologies. The actual cases represent recipes in which we classify ingredients according to culinary techniques applied to them. The complex nature of linguistic anaphoras in recipe texts make usual text mining techniques inefficient so a stronger approach, using syntactic and dynamic semantic analysis to build a formal representation of a recipe, had to be used. This representation is useful for various applications but, in this paper, we show how one can extract ingredient–action relations from it in order to use formal concept analysis and select an appropriate replacement sequence of culinary actions to use in adapting the recipe text.


formal concept analysis natural language processing text mining textual case-based reasoning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Asher, N.: Events, facts, propositions, and evolutive anaphora. In: Higginbotham, J., Pianesi, F., Varzi, A.C. (eds.) Speaking of events, pp. 123–150. OUP, Oxford (2000)Google Scholar
  2. 2.
    Asiimwe, S., Craw, S., Wiratunga, N., Taylor, B.: Automatically acquiring structured case representations: The SMART way. In: Applications and Innovations in Intelligent Systems, vol. XV, pp. 45–58. Springer, Heidelberg (2007)Google Scholar
  3. 3.
    Muñoz-Avila, H., Weberskirch, F.: Planning for manufacturing workpieces by storing, indexing and replaying planning decisions. In: Drabble, B. (ed.) Proceedings of the Third International Conference on Artificial Intelligence Planning Systems (AIPS 1996), Edinburgh, Scotland, May 1996, pp. 158–165 (1996)Google Scholar
  4. 4.
    Badra, F., Bendaoud, R., Bentebitel, R., Champin, P., Cojan, J., Cordier, A., Després, S., Jean-Daubias, S., Lieber, J., Meilender, T., Meilender, T., Mille, A., Nauer, E., Napoli, A., Toussaint, Y.: Taaable: Text Mining, Ontology Engineering, and Hierarchical Classification for Textual Case-Based Cooking. In: ECCBR Workshops, Workshop of the First Computer Cooking Contest, pp. 219–228. Springer, Heidelberg (2008)Google Scholar
  5. 5.
    Banko, M., Brill, E.: Mitigating the paucity-of-data problem: Exploring the effect of training corpus size on classifier. In: Proceedings of the first international conference on Human language technology research. Association for Computational Linguistics, Morristown (2001)Google Scholar
  6. 6.
    Brill, E.: Transformation-Based Learning. Ph.D. thesis, Univ. of Pennsylvania (1993)Google Scholar
  7. 7.
    Carpineto, C., Romamo, G.: Order-Theoretical Ranking. Journal of the American Society for Information Science 51(7), 587–601 (2000)CrossRefGoogle Scholar
  8. 8.
    Cordier, A., Lieber, J., Molli, P., Nauer, E., Skaf-Molli, H., Toussaint, Y.: Wiki-Taaable: A semantic wiki as a blackboard for a textual case-based reasoning system. In: 4th Workshop on Semantic Wikis (SemWiki 2009). 6th European Semantic Web Conference, Heraklion, pp. 88–101 (2009)Google Scholar
  9. 9.
    Díaz-Agudo, B., Gerváz, P., González-Calero, P.A.: Adaptation Guided Retrieval Based on Formal Concept Analysis. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 131–145. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Díaz-Agudo, B., González-Calero, P.A.: Classification Based Retrieval Using Formal Concept Analysis. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 173–188. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Ganter, B., Wille, R.: Formal Concept Analysis. Springer, Heidelberg (1999)zbMATHGoogle Scholar
  12. 12.
    Gervás, P., Hervás, R., Recio-García, J.: The Role of Natural Language Generation During Adaptation in Textual CBR. In: 4th Workshop on Textual Case-Based Reasoning: Beyond Retrieval, ICCBR 2007 (2007)Google Scholar
  13. 13.
    Ihrig, L., Kambhampati, S.: Derivation replay for partial-order planning. In: Proceedings of the 12th National Conference on Artificial Intelligence (AAAI 1994), pp. 992–997 (1994)Google Scholar
  14. 14.
    Lamontagne, L., Bentebibel, R., Miry, E., Despres, S.: Finding Lexical Relationships for the Reuse of Investigation Reports. In: 4th Workshop on Textual Case-Based Reasoning: Beyond Retrieval, ICCBR 2007 (2007)Google Scholar
  15. 15.
    Lamontagne, L., Lapalme, G.: Textual reuse for email response. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 234–246. Springer, Heidelberg (2004)Google Scholar
  16. 16.
    Marcus, M., Santorini, B., Marcinkiewicz, M.: Building a large annotated corpus of English: The Penn Treebank. Computational linguistics 19(2), 313–330 (1994)Google Scholar
  17. 17.
    Messai, N., Devignes, M.D., Napoli, A., Smaïl-Tabbone, M.: Querying a Bioinformatic Data Sources Registry with Concept Lattices. In: Dau, F., Mugnier, M.-L., Stumme, G. (eds.) ICCS, pp. 323–336. Springer, Heidelberg (2005)Google Scholar
  18. 18.
    Smyth, B., Keane, M.T.: Using adaptation knowledge to retrieve and adapt design cases. Knowledge-Based Systems 9(2), 127–135 (1996)CrossRefGoogle Scholar
  19. 19.
    Veloso, M. (ed.): Planning and Learning by Analogical Reasoning. LNCS (LNAI), vol. 886. Springer, Heidelberg (1994)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Valmi Dufour-Lussier
    • 1
  • Jean Lieber
    • 1
  • Emmanuel Nauer
    • 1
  • Yannick Toussaint
    • 1
  1. 1.LORIAUMR 7503 (CNRS, INRIA, Nancy-Université)Vandœuvre-lès-NancyFrance

Personalised recommendations