Abstract

This paper is dedicated to the development of a novel method for coordination analysis (CA) in English using the neural (deep learning) methods. An efficient solution for the task allows identifying potentially valuable links and relationships between specific parts of a sentence, making the extraction of coordinate structures an important text preprocessing tool. In this study, a number of ideas for approaching the task within the framework of one-stage detectors were tested. The achieved results are comparable in quality to the current most advanced CA methods while allowing to process more than three-fold more sentences per unit time.

中文翻译：

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用于协调分析的神经网络

摘要

本文致力于使用神经（深度学习）方法开发一种新的英语协调分析（CA）方法。该任务的有效解决方案可以识别句子特定部分之间潜在有价值的链接和关系，从而使坐标结构的提取成为重要的文本预处理工具。在这项研究中，测试了在一级探测器框架内完成任务的许多想法。所取得的结果在质量上可与当前最先进的 CA 方法相媲美，同时每单位时间处理的句子数量增加三倍以上。