This study presents a bibliometric analysis of industry--academia collaboration in artificial intelligence (AI) research, focusing on papers from two major international conferences, AAAI and IJCAI, from 2010 to 2023. Most previous studies have relied on publishers and other databases to analyze bibliographic information. However, these databases have problems, such as missing articles and omitted metadata. Therefore, we adopted a novel approach to extract bibliographic information directly from the article PDFs: we examined 20,549 articles and identified the collaborative papers through a classification process of author affiliation. The analysis explores the temporal evolution of collaboration in AI, highlighting significant changes in collaboration patterns over the past decade. In particular, this study examines the role of key academic and industrial institutions in facilitating these collaborations, focusing on emerging global trends. Additionally, a content analysis using document classification was conducted to examine the type of first author in collaborative research articles and explore the potential differences between collaborative and noncollaborative research articles. The results showed that, in terms of publication, collaborations are mainly led by academia, but their content is not significantly different from that of others. The affiliation metadata are available at https://github.com/mm-doshisha/ICADL2024.

翻译：本研究对人工智能（AI）研究领域的产学合作进行了文献计量分析，重点关注2010年至2023年间AAAI和IJCAI两大国际顶级会议的论文。以往多数研究依赖出版商及其他数据库来分析文献信息，但这些数据库存在文章缺失、元数据遗漏等问题。为此，我们采用了一种新颖的方法，直接从文章PDF文件中提取文献信息：通过作者所属机构的分类流程，我们对20,549篇文章进行了审查，并识别出合作论文。该分析探讨了AI领域合作的时间演变，突显了过去十年间合作模式的显著变化。本研究特别考察了关键学术与产业机构在促进这些合作中的作用，并聚焦于新兴的全球趋势。此外，通过采用文档分类技术进行内容分析，本研究考察了合作研究论文中第一作者的类型，并探讨了合作与非合作研究论文之间的潜在差异。结果表明，在发表方面，合作主要由学术界主导，但其内容与其他论文并无显著差异。所属机构元数据可在https://github.com/mm-doshisha/ICADL2024获取。