@article{KrestelChikkamathHeweletal.2021,
  author    = {Krestel, Ralf and Chikkamath, Renukswamy and Hewel, Christoph and Risch, Julian},
  title     = {A survey on deep learning for patent analysis},
  series = {World patent information},
  volume    = {65},
  journal   = {World patent information},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0172-2190},
  doi       = {10.1016/j.wpi.2021.102035},
  pages     = {13},
  year      = {2021},
  abstract  = {Patent document collections are an immense source of knowledge for research and innovation communities worldwide. The rapid growth of the number of patent documents poses an enormous challenge for retrieving and analyzing information from this source in an effective manner. Based on deep learning methods for natural language processing, novel approaches have been developed in the field of patent analysis. The goal of these approaches is to reduce costs by automating tasks that previously only domain experts could solve. In this article, we provide a comprehensive survey of the application of deep learning for patent analysis. We summarize the state-of-the-art techniques and describe how they are applied to various tasks in the patent domain. In a detailed discussion, we categorize 40 papers based on the dataset, the representation, and the deep learning architecture that were used, as well as the patent analysis task that was targeted. With our survey, we aim to foster future research at the intersection of patent analysis and deep learning and we conclude by listing promising paths for future work.},
  language  = {en}
}
@article{RischKrestel2020,
  author    = {Risch, Julian and Krestel, Ralf},
  title     = {Toxic comment detection in online discussions},
  series = {Deep learning-based approaches for sentiment analysis},
  journal   = {Deep learning-based approaches for sentiment analysis},
  editor    = {Agarwal, Basant and Nayak, Richi and Mittal, Namita and Patnaik, Srikanta},
  publisher = {Springer},
  address   = {Singapore},
  isbn      = {978-981-15-1216-2},
  issn      = {2524-7565},
  doi       = {10.1007/978-981-15-1216-2_4},
  pages     = {85 -- 109},
  year      = {2020},
  abstract  = {Comment sections of online news platforms are an essential space to express opinions and discuss political topics. In contrast to other online posts, news discussions are related to particular news articles, comments refer to each other, and individual conversations emerge. However, the misuse by spammers, haters, and trolls makes costly content moderation necessary. Sentiment analysis can not only support moderation but also help to understand the dynamics of online discussions. A subtask of content moderation is the identification of toxic comments. To this end, we describe the concept of toxicity and characterize its subclasses. Further, we present various deep learning approaches, including datasets and architectures, tailored to sentiment analysis in online discussions. One way to make these approaches more comprehensible and trustworthy is fine-grained instead of binary comment classification. On the downside, more classes require more training data. Therefore, we propose to augment training data by using transfer learning. We discuss real-world applications, such as semi-automated comment moderation and troll detection. Finally, we outline future challenges and current limitations in light of most recent research publications.},
  language  = {en}
}