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Enhancing Domain-Specific Supervised Natural Language Intent Classification with a Top-Down Selective Ensemble Model

1
Abaka Holdings Ltd. 1, London SW1E 5HX, UK
2
The Alan Turing Institute, London NW1 2DB, UK
3
Theoretical and Applied Linguistics, Faculty of Modern and Medieval Languages, University of Cambridge, Cambridge CB3 9DB, UK
*
Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2019, 1(2), 630-640; https://doi.org/10.3390/make1020037
Received: 1 March 2019 / Revised: 12 April 2019 / Accepted: 16 April 2019 / Published: 18 April 2019
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Abstract

Natural Language Understanding (NLU) systems are essential components in many industry conversational artificial intelligence applications. There are strong incentives to develop a good NLU capability in such systems, both to improve the user experience and in the case of regulated industries for compliance reasons. We report on a series of experiments comparing the effects of optimizing word embeddings versus implementing a multi-classifier ensemble approach and conclude that in our case, only the latter approach leads to significant improvements. The study provides a high-level primer for developing NLU systems in regulated domains, as well as providing a specific baseline accuracy for evaluating NLU systems for financial guidance. View Full-Text
Keywords: natural language understanding; dialogue systems; multi-classifier; word embeddings; domain adaptation; conversational artificial intelligence; financial domain; long short-term memory natural language understanding; dialogue systems; multi-classifier; word embeddings; domain adaptation; conversational artificial intelligence; financial domain; long short-term memory
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Jenset, G.B.; McGillivray, B. Enhancing Domain-Specific Supervised Natural Language Intent Classification with a Top-Down Selective Ensemble Model. Mach. Learn. Knowl. Extr. 2019, 1, 630-640.

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Mach. Learn. Knowl. Extr. EISSN 2504-4990 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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