Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation

• URL: http://arxiv.org/abs/2108.12899v1
• Date: Sun, 29 Aug 2021 19:41:35 GMT
• Title: Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation
• Authors: Chenkai Sun, Weijiang Li, Jinfeng Xiao, Nikolaus Nova Parulian, ChengXiang Zhai, Heng Ji
• Abstract summary: How to extract detailed knowledge about chemical reactions from the core chemistry literature is a new emerging challenge. We propose a novel multi-modal representation learning framework to solve the problem of fine-grained chemical entity typing. Experiment results show that the proposed framework outperforms multiple state-of-the-art methods.
• Score: 36.6963949360594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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