arXiv:1911.12224
Multi-label Classification for Automatic Tag Prediction in the Context of Programming Challenges
원문 초록
One of the best ways for developers to test and improve their skills in a fun and challenging way are programming challenges, offered by a plethora of websites. For the inexperienced ones, some of the problems might appear too challenging, requiring some suggestions to implement a solution. On the other hand, tagging problems can be a tedious task for problem creators. In this paper, we focus on automating the task of tagging a programming challenge description using machine and deep learning methods. We observe that the deep learning methods implemented outperform well-known IR approaches such as tf-idf, thus providing a starting point for further research on the task.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity
FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning Techniques
Convolutional Neural Networks (CNNs) for power system big data analysis
LLSF - Learning Label Specific Features for Multi-Label Classifcation
PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
GenIC: An LLM-Based Framework for Instance Completion in Knowledge Graphs
토론
이 논문과 연결된 포럼 글이 아직 없습니다.