Tag Prediction of Competitive Programming Problems using Deep Learning
  Techniques
Taha Lokat Divyam Prajapati Shubhada Labde
In the past decade, the amount of research being done in the fields of
machine learning and deep learning, predominantly in the area of natural
language processing (NLP), has risen dramatically. A well-liked method for
developing programming abilities like logic building and problem solving is
competitive programming. It can be tough for novices and even veteran
programmers to traverse the wide collection of questions due to the massive
number of accessible questions and the variety of themes, levels of difficulty,
and questions offered. In order to help programmers find questions that are
appropriate for their knowledge and interests, there is a need for an automated
method. This can be done using automated tagging of the questions using Text
Classification. Text classification is one of the important tasks widely
researched in the field of Natural Language Processing. In this paper, we
present a way to use text classification techniques to determine the domain of
a competitive programming problem. A variety of models, including are
implemented LSTM, GRU, and MLP. The dataset has been scraped from Codeforces, a
major competitive programming website. A total of 2400 problems were scraped
and preprocessed, which we used as a dataset for our training and testing of
models. The maximum accuracy reached using our model is 78.0% by MLP(Multi
Layer Perceptron).