Towards Kikamba Computational Grammar

Benson Kituku, Wanjiku Ng'ang'a, Lawrence Muchemi
{"title":"Towards Kikamba Computational Grammar","authors":"Benson Kituku, Wanjiku Ng'ang'a, Lawrence Muchemi","doi":"10.4236/jdaip.2019.74015","DOIUrl":null,"url":null,"abstract":"The under-resourced Kikamba language has few language technology tools since the more efficient and popular data driven approaches for developing them suffer from data sparseness due to lack of digitized corpora. To address this challenge, we have developed a computational grammar for the Kikamba language within the multilingual Grammatical Framework (GF) toolkit. GF uses the Interlingua rule-based translation approach. To develop the grammar, we used the morphology driven strategy. Therefore, we first developed regular expressions for morphology inflection and thereafter developed the syntax rules. Evaluation of the grammar was done using one hundred sentences in both English and Kikamba languages. The results were an encouraging four n-gram BLEU score of 83.05% and the Position independent error rate (PER) of 10.96%. Finally, we have made a contribution to the language technology resources for Kikamba including multilingual machine translation, a morphology analyzer, a computational grammar which provides a platform for development of multilingual applications and the ability to generate a variety of bilingual corpora for Kikamba for all languages currently defined in GF, making it easier to experiment with data driven approaches.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"数据分析和信息处理(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.4236/jdaip.2019.74015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

The under-resourced Kikamba language has few language technology tools since the more efficient and popular data driven approaches for developing them suffer from data sparseness due to lack of digitized corpora. To address this challenge, we have developed a computational grammar for the Kikamba language within the multilingual Grammatical Framework (GF) toolkit. GF uses the Interlingua rule-based translation approach. To develop the grammar, we used the morphology driven strategy. Therefore, we first developed regular expressions for morphology inflection and thereafter developed the syntax rules. Evaluation of the grammar was done using one hundred sentences in both English and Kikamba languages. The results were an encouraging four n-gram BLEU score of 83.05% and the Position independent error rate (PER) of 10.96%. Finally, we have made a contribution to the language technology resources for Kikamba including multilingual machine translation, a morphology analyzer, a computational grammar which provides a platform for development of multilingual applications and the ability to generate a variety of bilingual corpora for Kikamba for all languages currently defined in GF, making it easier to experiment with data driven approaches.
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
Kikamba计算语法研究
由于缺乏数字化语料库,开发Kikamba语言的更有效和流行的数据驱动方法受到数据稀疏的影响,因此资源不足的Kikamba语言几乎没有语言技术工具。为了应对这一挑战,我们在多语言语法框架(GF)工具包中为Kikamba语言开发了一个计算语法。GF使用Interlingua基于规则的翻译方法。为了开发语法,我们使用了词法驱动策略。因此,我们首先开发了词形变化的正则表达式,然后开发了语法规则。使用英语和基坎巴语的100个句子对语法进行了评估。结果令人鼓舞的4 n-gram BLEU得分为83.05%,位置无关错误率(PER)为10.96%。最后,我们为Kikamba的语言技术资源做出了贡献,包括多语言机器翻译,形态学分析仪,计算语法,它为多语言应用程序的开发提供了一个平台,并能够为Kikamba生成各种双语语料库,用于目前在GF中定义的所有语言,使其更容易实验数据驱动的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
91
期刊最新文献
A Hybrid Neural Network Model Based on Transfer Learning for Forecasting Forex Market Enhancing Police Officers’ Cybercrime Investigation Skills Using a Checklist Tool A Sufficient Statistical Test for Dynamic Stability Lung Cancer Prediction from Elvira Biomedical Dataset Using Ensemble Classifier with Principal Component Analysis Modelling Key Population Attrition in the HIV and AIDS Programme in Kenya Using Random Survival Forests with Synthetic Minority Oversampling Technique-Nominal Continuous
×
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