巴厘岛故事文本数据集用于叙事文本分析
I Made Satria Bimantara1, Diana Purwitasari1, Ngurah Agus Sanjaya Er2
1Informatics Department, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia.
Data in brief
|September 10, 2024
概括
这项研究引入了第一个注释巴厘岛故事数据集用于计算语言工具,使低资源语言的字符识别和分类成为可能. 该数据集有助于开发用于叙事文本分析的先进机器学习模型.
科学领域:
- 计算语言学 计算语言学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 叙事文本分析的计算语言工具正在进步,但由于其他语言的数据稀缺,它们主要以英语为重点.
- 角色识别是深入叙事分析的关键第一步,但在不同的语言背景下仍然具有挑战性.
- 像巴厘岛这样的低资源语言缺乏足够的注释数据集来开发这种分析工具.
研究的目的:
- 介绍第一个注释的巴厘岛故事文本数据集用于叙事文本分析.
- 为了促进角色识别,别名集群 (命名实体链接),以及在巴厘岛叙事中的角色分类.
- 支持用于低资源语言的计算语言工具和机器学习模型的开发.
主要方法:
- 120个巴厘岛故事的手动注释由母语者,包括社会语言学专家.
- 创建四个子数据集,用于字符识别 (单词和句子级),别名聚类和主角/对手分类.
- 使用科恩卡帕,贾卡德相似度和平均F1分数来计算注释者之间的协议,以确保数据集的可靠性.
主要成果:
- 一个包括89,917个注释词,6,634个注释句和930个字符组的综合数据集.
- 成功注释角色识别,别名集群,并将848个角色组分为主角 (66.16%) 和对手 (33.84%).
- 数据集的可靠性和一致性通过高的注释者间协议得分来证明.
结论:
- 开发的巴厘岛叙事数据集是推进低资源语言的计算语言学和人工智能的宝贵资源.
- 这一数据集使得在角色识别,网络开发和在叙事文本中的关系提取方面进行了增强的研究.
- 它为构建复杂的机器学习和深度学习模型,分析非英语叙事结构提供了基础.
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