Open Access

ARTICLE

Number Entities Recognition in Multiple Rounds of Dialogue Systems

by Shan Zhang¹, Bin Cao¹, Yueshen Xu^2,*, Jing Fan¹

1 School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China
2 School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

* Corresponding Author: Yueshen Xu. Email:

(This article belongs to the Special Issue: Innovation and Application of Intelligent Processing of Data, Information and Knowledge in E-Commerce)

Computer Modeling in Engineering & Sciences 2021, 127(1), 309-323. https://doi.org/10.32604/cmes.2021.014802

Received 30 October 2020; Accepted 02 February 2021; Issue published 30 March 2021

Abstract

As a representative technique in natural language processing (NLP), named entity recognition is used in many tasks, such as dialogue systems, machine translation and information extraction. In dialogue systems, there is a common case for named entity recognition, where a lot of entities are composed of numbers, and are segmented to be located in different places. For example, in multiple rounds of dialogue systems, a phone number is likely to be divided into several parts, because the phone number is usually long and is emphasized. In this paper, the entity consisting of numbers is named as number entity. The discontinuous positions of number entities result from many reasons. We find two reasons from real-world dialogue systems. The first reason is the repetitive confirmation of different components of a number entity, and the second reason is the interception of mood words. The extraction of number entities is quite useful in many tasks, such as user information completion and service requests correction. However, the existing entity extraction methods cannot extract entities consisting of discontinuous entity blocks. To address these problems, in this paper, we propose a comprehensive method for number entity recognition, which is capable of extracting number entities in multiple rounds of dialogues systems. We conduct extensive experiments on a real-world dataset, and the experimental results demonstrate the high performance of our method.

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Keywords

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