基于双向LSTM神经网络的多候选分词

Theerapat Lapjaturapit, Kobkrit Viriyayudhakom, T. Theeramunkong
{"title":"基于双向LSTM神经网络的多候选分词","authors":"Theerapat Lapjaturapit, Kobkrit Viriyayudhakom, T. Theeramunkong","doi":"10.1109/ICESIT-ICICTES.2018.8442053","DOIUrl":null,"url":null,"abstract":"Most existing word segmentation methods output one single segmentation solution. This paper provides an analysis of word segmentation performance when more than one solutions are taken into account. Towards this investigation, a deep neural network with multiple thresholds is applied to generate multiple candidates for segmentation. As a test-bed, the well-known bidirectional long short-term memory (BiLSTM) units are used with eleven contexts in a deep neural network. As performance indices, three measures; recall, precision and f-measure, are plotted with respect to various thresholds for both boundary level and word level evaluation. By a number of experiments, the result shows that the multi-candidate word segmentation can help us increase the recalls while maintaining the precisions.","PeriodicalId":57136,"journal":{"name":"单片机与嵌入式系统应用","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Multi-Candidate Word Segmentation using Bi-directional LSTM Neural Networks\",\"authors\":\"Theerapat Lapjaturapit, Kobkrit Viriyayudhakom, T. Theeramunkong\",\"doi\":\"10.1109/ICESIT-ICICTES.2018.8442053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Most existing word segmentation methods output one single segmentation solution. This paper provides an analysis of word segmentation performance when more than one solutions are taken into account. Towards this investigation, a deep neural network with multiple thresholds is applied to generate multiple candidates for segmentation. As a test-bed, the well-known bidirectional long short-term memory (BiLSTM) units are used with eleven contexts in a deep neural network. As performance indices, three measures; recall, precision and f-measure, are plotted with respect to various thresholds for both boundary level and word level evaluation. By a number of experiments, the result shows that the multi-candidate word segmentation can help us increase the recalls while maintaining the precisions.\",\"PeriodicalId\":57136,\"journal\":{\"name\":\"单片机与嵌入式系统应用\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"单片机与嵌入式系统应用\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1109/ICESIT-ICICTES.2018.8442053\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"单片机与嵌入式系统应用","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/ICESIT-ICICTES.2018.8442053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

摘要

大多数现有的分词方法输出一个单一的分词解。本文提供了在考虑多个解决方案时的分词性能分析。针对这一研究,应用了一个具有多个阈值的深度神经网络来生成多个候选的分割。作为实验平台,我们将双向长短期记忆(BiLSTM)单元用于深度神经网络的11个上下文。作为绩效指标,有三个衡量标准;召回率、精度和f-measure都是根据边界水平和词水平评估的不同阈值绘制的。实验结果表明,多候选词分词可以在保证分词精度的前提下提高查全率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Candidate Word Segmentation using Bi-directional LSTM Neural Networks
Most existing word segmentation methods output one single segmentation solution. This paper provides an analysis of word segmentation performance when more than one solutions are taken into account. Towards this investigation, a deep neural network with multiple thresholds is applied to generate multiple candidates for segmentation. As a test-bed, the well-known bidirectional long short-term memory (BiLSTM) units are used with eleven contexts in a deep neural network. As performance indices, three measures; recall, precision and f-measure, are plotted with respect to various thresholds for both boundary level and word level evaluation. By a number of experiments, the result shows that the multi-candidate word segmentation can help us increase the recalls while maintaining the precisions.
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
7395
×
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