基于关系三重提取和新型数据增强方法的在线评论系统

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI:10.1145/3579654.3579741

Yufei Song, Junyang Mo, Zhongming Pan

{"title":"基于关系三重提取和新型数据增强方法的在线评论系统","authors":"Yufei Song, Junyang Mo, Zhongming Pan","doi":"10.1145/3579654.3579741","DOIUrl":null,"url":null,"abstract":"The online review system is a fundamental application system. However, the existing system not only can not automatically check the matching of comments and score ratings but also can not give a fair reference score according to the comments, In this work, we utilize a relational triple extraction method to solve the two problems for the first time. In addition, considering that the existing online review systems are generally characterized by a lack of high-quality labeled data, we present five novel data augmentation techniques for boosting performance specifically on relational triple extraction tasks. The five data augmentation techniques demonstrate particularly strong results for both datasets of the review system and the public datasets of relational triple extraction.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Online Review System Using Relational Triple Extraction with Novel Data Augmentation Methods\",\"authors\":\"Yufei Song, Junyang Mo, Zhongming Pan\",\"doi\":\"10.1145/3579654.3579741\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The online review system is a fundamental application system. However, the existing system not only can not automatically check the matching of comments and score ratings but also can not give a fair reference score according to the comments, In this work, we utilize a relational triple extraction method to solve the two problems for the first time. In addition, considering that the existing online review systems are generally characterized by a lack of high-quality labeled data, we present five novel data augmentation techniques for boosting performance specifically on relational triple extraction tasks. The five data augmentation techniques demonstrate particularly strong results for both datasets of the review system and the public datasets of relational triple extraction.\",\"PeriodicalId\":146783,\"journal\":{\"name\":\"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3579654.3579741\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3579654.3579741","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

在线审核系统是一个基本的应用系统。然而，现有的系统不仅不能自动检查评论和评分的匹配性，而且不能根据评论给出公平的参考分数，在这项工作中，我们首次利用关系三重提取方法解决了这两个问题。此外，考虑到现有的在线评论系统通常缺乏高质量的标记数据，我们提出了五种新的数据增强技术来提高性能，特别是在关系三重提取任务上。这五种数据增强技术对审查系统的数据集和关系三重提取的公共数据集都显示出特别强的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Online Review System Using Relational Triple Extraction with Novel Data Augmentation Methods

The online review system is a fundamental application system. However, the existing system not only can not automatically check the matching of comments and score ratings but also can not give a fair reference score according to the comments, In this work, we utilize a relational triple extraction method to solve the two problems for the first time. In addition, considering that the existing online review systems are generally characterized by a lack of high-quality labeled data, we present five novel data augmentation techniques for boosting performance specifically on relational triple extraction tasks. The five data augmentation techniques demonstrate particularly strong results for both datasets of the review system and the public datasets of relational triple extraction.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence

自引率

0.00%

发文量