Text adaptation using formal concept analysis

Valmi Dufour-Lussier (1) , Jean Lieber (1) , Emmanuel Nauer (1) , Yannick Toussaint (1)

1 ORPAILLEUR - Knowledge representation, reasonning

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.

Mots clés: formal concept analysis; natural language processing; text mining; textual case-based reasoning

Domaines: Intelligence artificielle [cs.AI]; Traitement du texte et du document

Valmi Dufour-Lussier, Jean Lieber, Emmanuel Nauer, Yannick Toussaint. Text adaptation using formal concept analysis. 18th International Conference on Case-Based Reasoning - ICCBR 2010, Jul 2010, Alessandria, Italy. pp.96-110, ⟨10.1007/978-3-642-14274-1_9⟩. ⟨hal-00509030⟩