On the Use of Dependencies in Relation Classification of Text with Deep Learning

Bernard Espinasse (1) , Sébastien Fournier (1) , Adrian Chifu (1) , Gaël Guibon (1) , René Azcurra , Valentin Mace

1 R2I - Recherche d’information et Interactions

Deep Learning is more and more used in NLP tasks, such as in relation classification of texts. This paper assesses the impact of syntactic dependencies in this task at two levels. The first level concerns the generic Word Embedding (WE) as input of the classification model, the second level concerns the corpus whose relations have to be classified. In this paper, two classification models are studied, the first one is based on a CNN using a generic WE and does not take into account the dependencies of the corpus to be treated, and the second one is based on a compositional WE combining a generic WE with syntactical annotations of this corpus to classify. The impact of dependencies in relation classification is estimated using two different WE. The first one is essentially lexical and trained on the Wikipedia corpus in English, while the second one is also syntactical, trained on the same previously annotated corpus with syntactical dependencies. The two classification models are evaluated on the SemEval 2010 reference corpus using these two generic WE. The experiments show the importance of taking dependencies into account at different levels in the relation classification.

Domaines: Intelligence artificielle [cs.AI]; Recherche d'information [cs.IR]; Traitement du texte et du document

Bernard Espinasse, Sébastien Fournier, Adrian Chifu, Gaël Guibon, René Azcurra, et al.. On the Use of Dependencies in Relation Classification of Text with Deep Learning. 20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing2019), Apr 2019, La Rochelle, France. ⟨hal-02103919⟩