# DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging

arXiv: https://arxiv.org/abs/1707.04596 · HTML: https://mlsanity.com/paper/1707.04596/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2017.

## Built on by (most cited first, 5 of 5)

- [MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification](https://mlsanity.com/paper/2212.12061/) (2022)
- [Efficient Data Access Paths for Mixed Vector-Relational Search](https://mlsanity.com/paper/2403.15807/) (2024)
- [TAG: Toward Accurate Social Media Content Tagging with a Concept Graph](https://mlsanity.com/paper/2110.06892/) (2021)
- [Optimizing Context-Enhanced Relational Joins](https://mlsanity.com/paper/2312.01476/) (2023)
- [Domain Representative Keywords Selection: A Probabilistic Approach](https://mlsanity.com/paper/2203.10365/) (2022)

## Abstract (arXiv)

Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit several downstream applications such as recommendation and search. In this work, we propose a novel yet simple approach called DocTag2Vec to accomplish this task. We substantially extend Word2Vec and Doc2Vec---two popular models for learning distributed representation of words and documents. In DocTag2Vec, we simultaneously learn the representation of words, documents, and tags in a joint vector space during training, and employ the simple k-nearest neighbor search to predict tags for unseen documents. In contrast to previous multi-label learning methods, DocTag2Vec directly deals with raw text instead of provided feature vector, and in addition, enjoys advantages like the learning of tag representation, and the ability of handling newly created tags. To demonstrate the effectiveness of our approach, we conduct experiments on several datasets and show promising results against state-of-the-art methods.