Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation
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DOI：
10.1109/bibm52615.2021.9669360
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Chenkai Sun;Weijian Li;Jinfeng Xiao;N. Parulian;ChengXiang Zhai;Heng Ji
中科院分区：
文献类型：
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作者：
Chenkai Sun;Weijian Li;Jinfeng Xiao;N. Parulian;ChengXiang Zhai;Heng Ji
从流行的化学文献中自动发现知识对于更有效的生物医学研究是必不可少的。如何从核心化学文献中提取有关化学反应的详细知识是一个新的挑战，尚未得到很好的研究。在本文中，我们研究了细粒度化学实体分类的新问题，这带来了有趣的新挑战，特别是由于复杂的名称经常出现在化学文献和实体的图形表示中。我们引入了一个新的基准数据集(Chemet)来促进这一新任务的研究，并提出了一个新的多通道表示学习框架，通过利用外部资源和化学结构来解决细粒度化学实体分类问题，并利用跨通道注意来学习化学领域中文本的有效表示。实验结果表明，该框架的性能优于现有的多种方法。1
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. 1