{"title":"用于协调分析的神经网络","authors":"A. I. Predelina, S. Yu. Dulikov, A. M. Alekseev","doi":"10.1134/S1064562423701181","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":531,"journal":{"name":"Doklady Mathematics","volume":"108 2 supplement","pages":"S416 - S423"},"PeriodicalIF":0.6000,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neural Networks for Coordination Analysis\",\"authors\":\"A. I. Predelina, S. Yu. Dulikov, A. M. Alekseev\",\"doi\":\"10.1134/S1064562423701181\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":531,\"journal\":{\"name\":\"Doklady Mathematics\",\"volume\":\"108 2 supplement\",\"pages\":\"S416 - S423\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2024-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Doklady Mathematics\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://link.springer.com/article/10.1134/S1064562423701181\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATHEMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Doklady Mathematics","FirstCategoryId":"100","ListUrlMain":"https://link.springer.com/article/10.1134/S1064562423701181","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0
摘要
摘要 本文致力于开发一种使用神经(深度学习)方法进行英语协调分析(CA)的新方法。对这一任务的有效解决方案可以识别句子特定部分之间潜在的有价值的联系和关系,从而使提取坐标结构成为重要的文本预处理工具。在本研究中,测试了在单级检测器框架内处理该任务的若干想法。所取得的结果在质量上可与目前最先进的 CA 方法相媲美,同时单位时间内可处理的句子数量增加了三倍以上。
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.
期刊介绍:
Doklady Mathematics is a journal of the Presidium of the Russian Academy of Sciences. It contains English translations of papers published in Doklady Akademii Nauk (Proceedings of the Russian Academy of Sciences), which was founded in 1933 and is published 36 times a year. Doklady Mathematics includes the materials from the following areas: mathematics, mathematical physics, computer science, control theory, and computers. It publishes brief scientific reports on previously unpublished significant new research in mathematics and its applications. The main contributors to the journal are Members of the RAS, Corresponding Members of the RAS, and scientists from the former Soviet Union and other foreign countries. Among the contributors are the outstanding Russian mathematicians.