Abstract
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.
The authors would like to thank the reviewers for their helpful comments, as well as Zeeshan Ahmed for his previous work on a related topic and Jenia Berzak, Le Dieu Thu, and Ripan Hermawan for letting us use their corpus of part-of-speech-tagged recipe texts.
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Dufour-Lussier, V., Lieber, J., Nauer, E., Toussaint, Y. (2010). Text Adaptation Using Formal Concept Analysis. In: Bichindaritz, I., Montani, S. (eds) Case-Based Reasoning. Research and Development. ICCBR 2010. Lecture Notes in Computer Science(), vol 6176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14274-1_9
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