Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation [article]

Chenkai Sun, Weijiang Li, Jinfeng Xiao, Nikolaus Nova Parulian, ChengXiang Zhai, Heng Ji
<span title="2021-08-29">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... sentation 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.12899v1">arXiv:2108.12899v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cib35n544vdo5pj34ztrm7jowy">fatcat:cib35n544vdo5pj34ztrm7jowy</a> </span>
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