# Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification

arXiv: https://arxiv.org/abs/1607.07956 · HTML: https://mlsanity.com/paper/1607.07956/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2016.

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

- [Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches](https://mlsanity.com/paper/2211.16285/) (2022)
- [Representation Learning via Variational Bayesian Networks](https://mlsanity.com/paper/2306.16326/) (2023)
- [Conformal Predictor for Improving Zero-shot Text Classification Efficiency](https://mlsanity.com/paper/2210.12619/) (2022)
- [Knowledge Graph Embeddings: A Comprehensive Survey on Capturing Relation Properties](https://mlsanity.com/paper/2410.14733/) (2024)
- [Exploring Description-Augmented Dataless Intent Classification](https://mlsanity.com/paper/2407.17862/) (2024)

## Abstract (arXiv)

Due to the lack of structured knowledge applied in learning distributed representation of cate- gories, existing work cannot incorporate category hierarchies into entity information. We propose a framework that embeds entities and categories into a semantic space by integrating structured knowledge and taxonomy hierarchy from large knowledge bases. The framework allows to com- pute meaningful semantic relatedness between entities and categories. Our framework can han- dle both single-word concepts and multiple-word concepts with superior performance on concept categorization and yield state of the art results on dataless hierarchical classification.