# Concept Tagging for Natural Language Understanding: Two Decadelong Algorithm Development

arXiv: https://arxiv.org/abs/1807.10661 · HTML: https://mlsanity.com/paper/1807.10661/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [TAG: Toward Accurate Social Media Content Tagging with a Concept Graph](https://mlsanity.com/paper/2110.06892/) (2021)

## Abstract (arXiv)

Concept tagging is a type of structured learning needed for natural language understanding (NLU) systems. In this task, meaning labels from a domain ontology are assigned to word sequences. In this paper, we review the algorithms developed over the last twenty five years. We perform a comparative evaluation of generative, discriminative and deep learning methods on two public datasets. We report on the statistical variability performance measurements. The third contribution is the release of a repository of the algorithms, datasets and recipes for NLU evaluation.