# Tag Prediction of Competitive Programming Problems using Deep Learning Techniques

arXiv: https://arxiv.org/abs/2308.01863 · HTML: https://mlsanity.com/paper/2308.01863/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [BAE: BERT-based Adversarial Examples for Text Classification](https://mlsanity.com/paper/2004.01970/) (2020)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [BertGCN: Transductive Text Classification by Combining GCN and BERT](https://mlsanity.com/paper/2105.05727/) (2021)
- [A pipeline and comparative study of 12 machine learning models for text classification](https://mlsanity.com/paper/2204.06518/) (2022)

## Abstract (arXiv)

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).