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arXiv · 1807.10661

Concept Tagging for Natural Language Understanding: Two Decadelong Algorithm Development

Jacopo Gobbi·Evgeny Stepanov·Giuseppe Riccardi
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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.

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Jacopo Gobbi, Evgeny Stepanov, Giuseppe Riccardi. 2018-07-27. Concept Tagging for Natural Language Understanding: Two Decadelong Algorithm Development. https://arxiv.org/abs/1807.10661

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