论文标题
在科学文本中确定人工智能的发展和应用
Identifying the Development and Application of Artificial Intelligence in Scientific Text
论文作者
论文摘要
我们描述了一种识别与人工智能应用和开发有关的研究出版物宇宙的策略。该方法利用了科学预印本的ARXIV语料库，在该研究中，作者从编辑器定义的集合中为论文选择主题标签。我们通过从纸质元数据中学习这些主题，然后推断出较大的语料库中论文的Arxiv-主体标签来构成AI相关性的功能定义：Clarivate of Science of Science of Science，数字科学维度和Microsoft Academic Graph。这产生了自然语言处理（CS.CL），计算机视觉（CS.CV）和Robotics（CS.RO）的0.75和.86之间的预测分类$ f_1 $得分。对于一个学习这些和其他四个与AI相关的主题（CS.AI，CS.LG，Stat.ml和Cs.ma）的单个模型，我们看到了.83的精度和.85的回忆。我们评估了分类器的室外性能与其他主题信息的其他来源以及替代方法的预测。我们发现，有监督的解决方案可以概括以识别属于ARXIV代表的高级研究领域的出版物。这提供了一种方法，可以识别与研究输出速度更新的与AI相关的出版物，而无需依赖于查询开发或标签的主题专家。
We describe a strategy for identifying the universe of research publications relevant to the application and development of artificial intelligence. The approach leverages the arXiv corpus of scientific preprints, in which authors choose subject tags for their papers from a set defined by editors. We compose a functional definition of AI relevance by learning these subjects from paper metadata, and then inferring the arXiv-subject labels of papers in larger corpora: Clarivate Web of Science, Digital Science Dimensions, and Microsoft Academic Graph. This yields predictive classification $F_1$ scores between .75 and .86 for Natural Language Processing (cs.CL), Computer Vision (cs.CV), and Robotics (cs.RO). For a single model that learns these and four other AI-relevant subjects (cs.AI, cs.LG, stat.ML, and cs.MA), we see precision of .83 and recall of .85. We evaluate the out-of-domain performance of our classifiers against other sources of topic information and predictions from alternative methods. We find that a supervised solution can generalize to identify publications that belong to the high-level fields of study represented on arXiv. This offers a method for identifying AI-relevant publications that updates at the pace of research output, without reliance on subject-matter experts for query development or labeling.
