Fu Lee Wang

TL;DR: A topic-level maximum entropy (TME) model is proposed for social emotion classification over short text that generates topic- level features by modeling latent topics, multiple emotion labels, and valence scored by numerous readers jointly.

TL;DR: This paper introduces two parallel sub-networks with a comprehensive loss function which synergize to derain and recover the lost details caused by deraining, and proposes an end-to-end detail-recovery image deraining network (termed a DRDNet) to solve the problem.

TL;DR: A bibliometric analysis of NLP-empowered medical research publications for uncovering the recent research status is presented to assist relevant researchers, especially newcomers in understanding the research development systematically, seeking scientific cooperation partners, optimizing research topic choices and monitoring new scientific or technological activities.

TL;DR: This is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems and a novel generic framework SenticRank is presented to incorporate various sentiment Information to various sentiment-based information for personalized search by user profiles and resource profiles.

TL;DR: Fractal summarization is developed based on the fractal theory and reduces the computation load in comparing with the generation of the entire summary in one batch by the traditional automatic summarization, which is ideal for wireless access.