OpenChemIE：面向化学文献的信息抽取工具包
OpenChemIE: An Information Extraction Toolkit For Chemistry Literature
AI总结：
OpenChemIE 提出文档级化学文献信息抽取工具包，通过多模态神经模型与化学信息学算法整合文本、表格和图形信息，实现细粒度反应数据抽取，并在反应式数据集上取得 69.5% F1 分数。
摘要：
从化学文献中抽取信息对于构建面向数据驱动化学的最新反应数据库至关重要。完整的抽取需要结合文本、表格和图形中的信息，而先前的工作主要研究从单一模态中抽取反应。在本文中，我们提出 OpenChemIE 来应对这一复杂挑战，并实现文档级别的反应数据抽取。OpenChemIE 分两步解决该问题：先从各个模态中抽取相关信息，然后整合结果以获得最终的反应列表。在第一步中，我们采用专门的神经模型，每个模型针对化学信息抽取中的特定任务，例如从文本或图形中解析分子或反应。然后，我们使用化学信息学算法整合这些模块的信息，从而能够从反应条件和底物范围研究中抽取出细粒度的反应数据。我们的机器学习模型在单独评估时达到了最先进的性能，并且我们精心标注了一个具有挑战性的含 R 基团的反应式数据集，以整体评估我们的流程，取得了 69.5% 的 F1 分数。此外，与 Reaxys 化学数据库直接比较时，OpenChemIE 的反应抽取结果达到了 64.3% 的准确率分数。我们以开源软件包的形式免费向公众提供 OpenChemIE，同时提供网页界面。
英文摘要：
Information extraction from chemistry literature is vital for constructing up-to-date reaction databases for data-driven chemistry. Complete extraction requires combining information across text, tables, and figures, whereas prior work has mainly investigated extracting reactions from single modalities. In this paper, we present OpenChemIE to address this complex challenge and enable the extraction of reaction data at the document level. OpenChemIE approaches the problem in two steps: extracting relevant information from individual modalities and then integrating the results to obtain a final list of reactions. For the first step, we employ specialized neural models that each address a specific task for chemistry information extraction, such as parsing molecules or reactions from text or figures. We then integrate the information from these modules using chemistry-informed algorithms, allowing for the extraction of fine-grained reaction data from reaction condition and substrate scope investigations. Our machine learning models attain state-of-the-art performance when evaluated individually, and we meticulously annotate a challenging dataset of reaction schemes with R-groups to evaluate our pipeline as a whole, achieving an F1 score of 69.5%. Additionally, the reaction extraction results of \ours attain an accuracy score of 64.3% when directly compared against the Reaxys chemical database. We provide OpenChemIE freely to the public as an open-source package, as well as through a web interface.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
