Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation
arXiv:2108.12899
Abstract
Automated knowledge discovery from trending chemical literature is essential for more efficient biomedical research. How to extract detailed knowledge about chemical reactions from the core chemistry literature is a new emerging challenge that has not been well studied. In this paper, we study the new problem of fine-grained chemical entity typing, which poses interesting new challenges especially because of the complex name mentions frequently occurring in chemistry literature and graphic representation of entities. We introduce a new benchmark data set (CHEMET) to facilitate the study of the new task and propose a novel multi-modal representation learning framework to solve the problem of fine-grained chemical entity typing by leveraging external resources with chemical structures and using cross-modal attention to learn effective representation of text in the chemistry domain. Experiment results show that the proposed framework outperforms multiple state-of-the-art methods.
References in corpus (6)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Learning Transferable Visual Models From Natural Language Supervision
- VisualBERT: A Simple and Performant Baseline for Vision and Language
- How Powerful are Graph Neural Networks?
- Simple BERT Models for Relation Extraction and Semantic Role Labeling
- ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning