# Machine learning approach for text and document mining

arXiv: https://arxiv.org/abs/1406.1580 · HTML: https://mlsanity.com/paper/1406.1580/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2014.

## Built on by (most cited first, 6 of 6)

- [KNN-Diffusion: Image Generation via Large-Scale Retrieval](https://mlsanity.com/paper/2204.02849/) (2022)
- [Better RAG using Relevant Information Gain](https://mlsanity.com/paper/2407.12101/) (2024)
- [Scalable Disk-Based Approximate Nearest Neighbor Search with Page-Aligned Graph](https://mlsanity.com/paper/2509.25487/) (2025)
- [Classification of Anuran Frog Species Using Machine Learning](https://mlsanity.com/paper/2112.05148/) (2021)
- [Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh](https://mlsanity.com/paper/2101.09667/) (2021)
- [A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts](https://mlsanity.com/paper/2204.02712/) (2022)

## Abstract (arXiv)

Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learning (ML) and has received much attention in the last years from both researchers in the academia and industry developers. In this paper, we first categorize the documents using KNN based machine learning approach and then return the most relevant documents.