支持向量机和隐马尔可夫模型在自然语言处理的名称实体识别中的应用

Jiaheng Li
{"title":"支持向量机和隐马尔可夫模型在自然语言处理的名称实体识别中的应用","authors":"Jiaheng Li","doi":"10.61173/brgdky68","DOIUrl":null,"url":null,"abstract":"This paper illustrates a comparison between the Hidden Markov Model and the Support Vector Machine, two important methodologies and tools, used in Natural Language Processing. Breaking down the model formulations of each, this paper first describes the mathematical motivations behind their applications in NLP. The mathematical motivations will be discussed through formulas, ideas, and examples. Then, this paper applies two real pre-established algorithms, one for each model, as examples to further rationalize their unique characteristics, similarities, and differences. These aspects will be broken down further into algorithmic efficiency, effectiveness, and other factors. Based on their performances analyzed through each factor, specific toolkits will be proposed, explained, and tested to optimize the test results, as the improving method. Some examples of toolkits include YamCha, TinySVM, etc. Overall, Name Entity Recognition involves different methodologies, and SVM and HMM, which represent two leading areas of NLP research, can best describe future trends and current situations.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"30 8","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Support Vector Machine and Hidden Markov Model in Name Entity Recognition of Natural Language Processing\",\"authors\":\"Jiaheng Li\",\"doi\":\"10.61173/brgdky68\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper illustrates a comparison between the Hidden Markov Model and the Support Vector Machine, two important methodologies and tools, used in Natural Language Processing. Breaking down the model formulations of each, this paper first describes the mathematical motivations behind their applications in NLP. The mathematical motivations will be discussed through formulas, ideas, and examples. Then, this paper applies two real pre-established algorithms, one for each model, as examples to further rationalize their unique characteristics, similarities, and differences. These aspects will be broken down further into algorithmic efficiency, effectiveness, and other factors. Based on their performances analyzed through each factor, specific toolkits will be proposed, explained, and tested to optimize the test results, as the improving method. Some examples of toolkits include YamCha, TinySVM, etc. Overall, Name Entity Recognition involves different methodologies, and SVM and HMM, which represent two leading areas of NLP research, can best describe future trends and current situations.\",\"PeriodicalId\":438278,\"journal\":{\"name\":\"Science and Technology of Engineering, Chemistry and Environmental Protection\",\"volume\":\"30 8\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science and Technology of Engineering, Chemistry and Environmental Protection\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.61173/brgdky68\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science and Technology of Engineering, Chemistry and Environmental Protection","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.61173/brgdky68","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

本文比较了隐马尔可夫模型和支持向量机这两种用于自然语言处理的重要方法和工具。本文首先介绍了这两种模型在 NLP 中应用背后的数学动机。本文将通过公式、观点和示例来讨论数学动机。然后,本文将以两个预先建立的真实算法为例,进一步说明它们的独特性、相似性和差异性。这些方面将进一步细分为算法效率、有效性和其他因素。在通过每个因素分析其性能的基础上,将提出、解释和测试具体的工具包,以优化测试结果,作为改进方法。这些工具包包括 YamCha、TinySVM 等。总之,名称实体识别涉及不同的方法,而 SVM 和 HMM 作为 NLP 研究的两个领先领域,最能说明未来的趋势和现状。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Support Vector Machine and Hidden Markov Model in Name Entity Recognition of Natural Language Processing
This paper illustrates a comparison between the Hidden Markov Model and the Support Vector Machine, two important methodologies and tools, used in Natural Language Processing. Breaking down the model formulations of each, this paper first describes the mathematical motivations behind their applications in NLP. The mathematical motivations will be discussed through formulas, ideas, and examples. Then, this paper applies two real pre-established algorithms, one for each model, as examples to further rationalize their unique characteristics, similarities, and differences. These aspects will be broken down further into algorithmic efficiency, effectiveness, and other factors. Based on their performances analyzed through each factor, specific toolkits will be proposed, explained, and tested to optimize the test results, as the improving method. Some examples of toolkits include YamCha, TinySVM, etc. Overall, Name Entity Recognition involves different methodologies, and SVM and HMM, which represent two leading areas of NLP research, can best describe future trends and current situations.
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
期刊最新文献
Improvement of EfficientNet in medical waste classification A Review of Research on Hospital Electronic Medical Record Management System Based on Cloud Computing Exploration of the Application of UAV Remote Sensing Technology in Engineering Surveying and Mapping Research on the Influencing factors of Heart Disease based on Binary Logistic Regression A review of YOLO-based traffic sign target detection
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1