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Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation

2021-08-29 19:41:35
Chenkai Sun, Weijiang Li, Jinfeng Xiao, Nikolaus Nova Parulian, ChengXiang Zhai, Heng Ji

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.

Abstract (translated)

URL

https://arxiv.org/abs/2108.12899

PDF

https://arxiv.org/pdf/2108.12899


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