# Automatic essay scoring: leveraging Jaccard coefficient and Cosine similaritywith n-gram variation in vector space model approach

arXiv: https://arxiv.org/abs/2510.15311 · HTML: https://mlsanity.com/paper/2510.15311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 97 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Evaluating Various Tokenizers for Arabic Text Classification](https://mlsanity.com/paper/2106.07540/) (2021)
- [Semantic Sensitive TF-IDF to Determine Word Relevance in Documents](https://mlsanity.com/paper/2001.09896/) (2020)
- [Automatic Short Answer Grading and Feedback Using Text Mining Methods](https://mlsanity.com/paper/1807.10543/) (2018)
- [Automatic assessment of text-based responses in post-secondary education: A systematic review](https://mlsanity.com/paper/2308.16151/) (2023)
- [t-SS3: a text classifier with dynamic n-grams for early risk detection over text streams](https://mlsanity.com/paper/1911.06147/) (2019)
- [SIDU-TXT: An XAI Algorithm for NLP with a Holistic Assessment Approach](https://mlsanity.com/paper/2402.03043/) (2024)
- [NLP Methods in Host-based Intrusion Detection Systems: A Systematic Review and Future Directions](https://mlsanity.com/paper/2201.08066/) (2022)
- [Differentiable N-gram Objective on Abstractive Summarization](https://mlsanity.com/paper/2202.04003/) (2022)
- [Semantics-driven Attentive Few-shot Learning over Clean and Noisy Samples](https://mlsanity.com/paper/2201.03043/) (2022)

## Abstract (arXiv)

Automated essay scoring (AES) is a vital area of research aiming to provide efficient and accurate assessment tools for evaluating written content. This study investigates the effectiveness of two popular similarity metrics, Jaccard coefficient, and Cosine similarity, within the context of vector space models(VSM)employing unigram, bigram, and trigram representations. The data used in this research was obtained from the formative essay of the citizenship education subject in a junior high school. Each essay undergoes preprocessing to extract features using n-gram models, followed by vectorization to transform text data into numerical representations. Then, similarity scores are computed between essays using both Jaccard coefficient and Cosine similarity. The performance of the system is evaluated by analyzing the root mean square error (RMSE), which measures the difference between the scores given by human graders and those generated by the system. The result shows that the Cosine similarity outperformed the Jaccard coefficient. In terms of n-gram, unigrams have lower RMSE compared to bigrams and trigrams.