Concept Tagging for Natural Language Understanding: Two Decadelong Algorithm Development

TL;DR

This paper reviews 25 years of concept tagging algorithms for natural language understanding, comparing various methods and providing a resource repository for future research.

Contribution

It offers a comprehensive review, comparative evaluation, and a publicly available repository of algorithms, datasets, and evaluation recipes for concept tagging in NLU.

Findings

Deep learning methods outperform traditional algorithms

Statistical variability impacts performance measurements

Repository facilitates future research and benchmarking

Abstract

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.