基于认知模型的汉语谓词自动分析

Shiqi Li, T. Zhao, Hanjing Li, Shui Liu, Pengyuan Liu
{"title":"基于认知模型的汉语谓词自动分析","authors":"Shiqi Li, T. Zhao, Hanjing Li, Shui Liu, Pengyuan Liu","doi":"10.1109/NLPKE.2010.5587843","DOIUrl":null,"url":null,"abstract":"This paper presents an cognitive approach to semantic role labeling in Chinese based on an extension of Construction-Integration (CI) model. The method can implicitly integrate more contextual and general knowledge into the calculating process in contrast with the machine learning methods. First, we define a proposition representation as the basic unit for semantic role labeling using CI model. Then the contextually appropriate propositions will be strengthened and inappropriate ones will be inhibited by simulating the spreading activation of human mind. Finally, experimental results show an encouraging performance on Chinese PropBank (CPB) and other two datasets.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using cognitive model to automatically analyze Chinese predicate\",\"authors\":\"Shiqi Li, T. Zhao, Hanjing Li, Shui Liu, Pengyuan Liu\",\"doi\":\"10.1109/NLPKE.2010.5587843\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an cognitive approach to semantic role labeling in Chinese based on an extension of Construction-Integration (CI) model. The method can implicitly integrate more contextual and general knowledge into the calculating process in contrast with the machine learning methods. First, we define a proposition representation as the basic unit for semantic role labeling using CI model. Then the contextually appropriate propositions will be strengthened and inappropriate ones will be inhibited by simulating the spreading activation of human mind. Finally, experimental results show an encouraging performance on Chinese PropBank (CPB) and other two datasets.\",\"PeriodicalId\":259975,\"journal\":{\"name\":\"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NLPKE.2010.5587843\",\"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 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NLPKE.2010.5587843","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

本文提出了一种基于构建-集成(CI)模型扩展的汉语语义角色标注认知方法。与机器学习方法相比,该方法可以隐式地将更多的上下文和一般知识集成到计算过程中。首先,我们定义了一个命题表示作为语义角色标注的基本单元。然后通过模拟人脑的扩张性激活,强化情境适宜命题,抑制情境不适宜命题。最后,实验结果表明,在中文PropBank (CPB)和其他两个数据集上取得了令人鼓舞的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using cognitive model to automatically analyze Chinese predicate
This paper presents an cognitive approach to semantic role labeling in Chinese based on an extension of Construction-Integration (CI) model. The method can implicitly integrate more contextual and general knowledge into the calculating process in contrast with the machine learning methods. First, we define a proposition representation as the basic unit for semantic role labeling using CI model. Then the contextually appropriate propositions will be strengthened and inappropriate ones will be inhibited by simulating the spreading activation of human mind. Finally, experimental results show an encouraging performance on Chinese PropBank (CPB) and other two datasets.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信