Humor is an important part of personal communication. How to create a computational model to recognize humor is still a very challenging task in natural language processing and linguistics. In this survey, we applied some rules to leave 29 articles spanning 10 years (2012 to 2023). The main elements covered by this survey include: recent state-of-the-art detection methods using deep learning from years 2012-2023, (2) summarizing features for humor detection from a linguistic perspective, (3) humor detection datasets, evaluation metrics, data domains and languages, (4) some tricks used in humor detection (e.g. Attention mechanism, multimodal), (5) recognizing open problems and highlight the feasible opportunities for future research directions. To the best of our knowledge, this is the first systematic survey for humor detection using deep learning. The survey can be used to assist novice and prominent researchers to understand the concept of humor, popular method and future research direction and so on.
Abstract Humor is an important part of personal communication. How to create a computational model to recognize humor is still a very challenging task in natural language processing [...]
MicroRNAs (miRNAs) play essential roles in various biological regulatory processes and are closely related to the occurrence and development of complex diseases. Identifying miRNA-disease associations (MDA) is of great value for revealing the molecular mechanisms of diseases and exploring therapeutic strategies and drug development. Recently, most computer-aided MDAs identification approaches design their models tend to base on a bipartite graph (i.e., miRNA-disease network), ignoring the endogenous RNAs(ceRNAs) hypothesis in post-transcriptional control such as gene negative regulation by targeting mRNAs. Besides, the existing MDA bipartite graph could not make convincing predictions for MDA, only relying on collaborative filtering followed by the recommended system. To address the above issues, we propose a TDCMDA (Tripartite graph-based integrating Dual-layer Contrast learning into graph neural network for MDA prediction), which aims to integrate dual-layer contrast learning into graph neural network under the miRNA-disease-gene tripartite graph. Different from the existing approaches, TDCMDA introduces not only rich biologic regulatory relationships hidden in ceRNAs by a tripartite graph but also employs self-supervised dual-layer contrast learning to alleviate sparse label disadvantage. TDCMDA can learn node feature representation across three subgraph spaces such that the link representation between miRNA and disease can be obtained more biology semantically. Comprehensive experiments indicate TDCMDA is superior to several state-of-the-art approaches, and the case studies show that TDCMDA can convincingly detect novel MDA pairs and can be a promising tool for MDA identification.
Abstract MicroRNAs (miRNAs) play essential roles in various biological regulatory processes and are closely related to the occurrence and development of complex diseases. Identifying [...]
Humor detection using deep learning in 10 years: A survey 
