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作 者:张艳琼 朱兆松[1,2] 赵晓驰 Zhang Yanqiong;Zhu Zhaosong;Zhao Xiaochi(School of Mathematics and Information Science,Nanjing Normal University of Special Education,Nanjing 210038,China;Braille and Sign Language Research Center,Nanjing Normal University of Special Education,Nanjing 210038,China;Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service,Nanjing 210023,China)
机构地区:[1]南京特殊教育师范学院数学与信息科学学院,南京210038 [2]南京特殊教育师范学院中国盲文手语研究院,南京210038 [3]江苏省数据工程与知识服务重点实验室,南京210023
出 处:《数据分析与知识发现》2023年第10期144-155,共12页Data Analysis and Knowledge Discovery
基 金:国家社会科学基金项目(项目编号:20BTQ065)的研究成果之一。
摘 要:【目的】通过提取和组织蕴含在多模态手语资源中的知识,创建适用于手语语言研究的手语多模态语料库,满足公众从繁杂的多模态数据中获取所需手语语言知识的迫切要求。【应用背景】针对手语信息化程度低、资源组织无序、手语知识难以利用等问题,构建适用于手语语言知识挖掘的多模态语料库。【方法】构建手语词汇多模态特征标注体系,制定手语词汇特征编码方案,实施多层次标注,并建立手语词汇图数据模型,利用图数据库Neo4j进行存储和可视化呈现。【结果】手语词汇数据来自国家手语词汇语料库,目前已完成10000余手语词汇多模态标注,实现构建多模态语料库的全部过程。【结论】本文多模态语料库增加手形、动作、表情、姿态等多模态知识检索,能极大地提升手语语料的使用价值。[Objective]This paper extracts and organizes knowledge from multimodal sign language resources and constructs a corpus for related research.It meets the public’s urgent demands to obtain sign language knowledge.[Context]The new multimodal corpus is suitable for mining sign language knowledge,which addresses low information levels,disordered resource organization,and difficult utilization of sign language knowledge.[Methods]Firstly,we constructed the multi-modal feature annotation system for sign language vocabulary.Secondly,we formulated the feature coding scheme of the vocabulary and implemented multi-level annotation.Finally,we established the graph model for sign language vocabulary and the Neo4j database to store and visualize.[Results]The vocabulary data are from the national sign language vocabulary corpus.Over 10000 sign language vocabulary multimodal annotation has been completed,and we realized the whole process of constructing a multimodal corpus.[Conclusions]The new corpus increases knowledge retrieval of hand shape,movement,expression,and posture,which greatly improves the usability of the sign language corpus.
分 类 号:TP391[自动化与计算机技术—计算机应用技术] G250[自动化与计算机技术—计算机科学与技术]
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