Graph Convolutional Networks for Chemical Relation Extraction
Darshini Mahendran, Christina Tang, Bridget Thomson McInnes · Companion Proceedings of the Web Conference 2022 · 2022
Extracting information regarding novel chemicals and chemical reactions from chemical patents plays a vital role in the chemical and pharmaceutical industry. Due to the increasing volume of chemical patents, there is an urgent need for automated solutions to extract relations between chemical compounds. Several studies have used models that apply attention mechanisms such as Bidirectional Encoder Representations from Transformers (BERT) to capture the contextual information within a text. However, these models do not capture the global information about a specific vocabulary. On the other hand, Graph Convolutional Networks (GCNs) capture global dependencies between terms within a corpus but not the local contextual information. In this work, we propose two novel approaches, GCN-Vanilla and GCN-BERT, for chemical relation extraction. GCN-Vanilla approach builds a single graph for the whole corpus based on word co-occurrence and sentence-word relations. Then, we model the graph with GCN to capture the global information and classify the sentence nodes. GCN-BERT approach combines GCN and BERT to capture both global and local information, and build together a final representation for relation extraction. We evaluate our approaches on the CLEF-2020 dataset. Our results show the combined GCN-BERT approach outperforms standalone BERT and GCN models, and achieves a higher F1 than that reported in our previous studies.