Cosine similarity to determine similarity measure: Study case in online essay assessment
By Alfirna Rizqi Lahitani, Adhistya Erna Permanasari, Noor Akhmad Setiawan. 2016 4th International Conference on Cyber and IT Service Management, 2016
358 citations recorded by the source corpus.
Development of technology in educational field brings the easier ways through the variety of facilitation for learning process, sharing files, giving assignment and assessment. Automated Essay Scoring (AES) is one of the development systems for determining a score automatically from text document source to facilitate the correction and scoring by utilizing applications that run on the computer. AES process is used to help the lecturers to score efficiently and effectively. Besides it can reduce the subjectivity scoring problem. However, implementation of AES depends on many factors and cases, such as language and mechanism of scoring process especially for essay scoring. A number of methods implemented for weighting the terms from document and reaching the solutions for handling comparative level between documents answer and expert's document still defined. In this research, we implemented the weighting of Term Frequency - Inverse Document Frequency (TF-IDF) method and Cosine Similarity with the measuring degree concept of similarity terms in a document. Tests carried out on a number of Indonesian text-based documents that have gone through the stage of pre-processing for data extraction purposes. This process results is in a ranking of the document weight that have closesness match level with expert's document.
Subfields: Artificial Intelligence, Computer Science Applications, Information Systems
View Paper: https://www.semanticscholar.org/paper/3dbd3f34aa6dd9eb694c1a677e7291c36ba093d2
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Citations in the corpus
- Evaluation of Retrieval-Augmented Generation: A Survey (2024, background): “Efforts to codify these advancements into standardized evaluation frameworks would significantly bolster the field, providing clearer benchmarks for progress and more directly aligning RAG system advancements with user needs and societal impacts.”
- Breaking Distortion-free Watermarks in Large Language Models (2025, unknown): “05 (Appendix B), PPL on par with ordinary model outputs and cosine similarity score close to 1 [15].”