Extracting food-drug interactions from scientific literature: relation clustering to address lack of data

Tsanta Randriatsitohaina (1) , Thierry Hamon (1, 2)

1 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
2 UP13 - Université Paris 13

Food-Drug Interaction (FDI) occurs when food and drug are taken simultaneously and cause unexpected effect. This paper tackles the problem of mining scientific literature in order to extract these interactions. We consider this problem as a relation extraction task which can be solved with classification method. Since Food-Drug Interactions need a fine-grained description with many relation types, we face the data sparseness and the lack of examples per type of relation. To address this issue, we propose an effective approach for grouping relations sharing similar representation into clusters and reducing the lack of examples. Cluster labels are then used as labels of the dataset given to classifiers for the FDI type identification. Our approach, relying on the extraction of relevant features before, between, and after the entities associated by the relation, improves significantly the performance of the FDI classification. Finally, we contrast an intuitive grouping method based on the definition of the relation types and a unsupervised clustering based on the instances of each relation type.

Mots clés: Food-Drug Interaction; Medical text; Machine learning; Clustering

Domaines: Informatique [cs]; Informatique et langage [cs.CL]

Tsanta Randriatsitohaina, Thierry Hamon. Extracting food-drug interactions from scientific literature: relation clustering to address lack of data. International Conference on Intelligent Text Processing and Computational Linguistics, Apr 2019, La Rochelle, France. ⟨hal-02122766⟩