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In this work we evaluate applicability of entity pair models and neural network architectures for relation extraction and classification in scientific papers at SemEval-2018. We carry out experiments with representing entity pairs through sentence tokens and through shortest path in dependency tree, comparing approaches based on convolutional and recurrent neural networks. With convolutional network applied to shortest path in dependency tree we managed to be ranked eighth in subtask 1.1 (“clean data”), ninth in 1.2 (“noisy data”). Similar model applied to separate parts of the shortest path was mounted to ninth (extraction track) and seventh (classification track) positions in subtask 2 ranking.

Texterra at SemEval-2018 Task 7: Exploiting Syntactic Information for Relation Extraction and Classification in Scientific Papers