Nguyen Dong, Liakata Maria, DeDeo Simon, Eisenstein Jacob, Mimno David, Tromble Rebekah, Winters Jane
Alan Turing Institute, London, United Kingdom.
Institute for Language, Cognition and Computation, School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom.
Front Artif Intell. 2020 Aug 25;3:62. doi: 10.3389/frai.2020.00062. eCollection 2020.
In this article we describe our experiences with computational text analysis involving rich social and cultural concepts. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of key questions that can guide work in this area. Our guidance is based on our own experiences and is therefore inherently imperfect. Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that resonate for many. This leads to our final goal: to help promote interdisciplinary collaborations. Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis involving social and cultural concepts, and the more we bridge these divides, the more fruitful we believe our work will be.
在本文中，我们描述了我们在涉及丰富社会和文化概念的计算文本分析方面的经验。我们希望实现三个主要目标。首先，我们旨在阐明那些在关于计算文本分析方法的讨论中并非总是处于前沿的棘手问题。其次，我们希望提供一组关键问题，以指导该领域的工作。我们的指导基于我们自己的经验，因此本质上是不完美的。尽管如此，鉴于我们多样的学科背景和研究实践，我们希望涵盖一系列观点，并识别出许多人都认同的共性。这就引出了我们的最终目标：帮助促进跨学科合作。跨学科的见解和伙伴关系对于充分发挥任何涉及社会和文化概念的计算文本分析的潜力至关重要，而且我们跨越这些分歧的程度越大，我们相信我们的工作就会越有成效。
