Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Proteomics01:33

Proteomics

7.2K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

MADOran: A morphologically annotated dataset of Oran.

Data in brief·2025
Same author

Integrating artificial intelligence with Gamma Knife radiosurgery in treating meningiomas and schwannomas: a review.

Neurosurgical review·2025
Same author

Arabic punctuation dataset.

Data in brief·2024
Same journal

A harmonized fast-fashion garment-variant dataset for textile circularity and sustainability assessment.

Data in brief·2026
Same journal

Terahertz reflectivity dataset: Reading text on both sides of the page.

Data in brief·2026
Same journal

High-quality draft genome sequence data of <i>Levilactobacillus brevis</i> 3LB isolated from fermented milk koumiss.

Data in brief·2026
Same journal

Interview dataset: Encouraging the development of industrial symbiosis networks in Slovenia - transition to the circular economy.

Data in brief·2026
Same journal

Timeseries of multispectral and radar data and vegetation indices from Sentinel-1, Sentinel-2 and Landsat-8 at field scale.

Data in brief·2026
Same journal

BACI-VI-Bench: A dataset of variational inequality benchmark instances for multi-agent trade-network equilibrium.

Data in brief·2026
查看所有相关文章

相关实验视频

Updated: Jun 1, 2025

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms
05:12

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

674

经过形态分析和语法注释的古兰经数据集.

Majdi Sawalha1,2, Faisal Al-Shargi3, Sane Yagi4,5

  • 1College of Engineering, Al-Ain University, Abu Dhabi, UAE.

Data in brief
|January 20, 2025
PubMed
概括
此摘要是机器生成的。

形态分析和语法注释的"古兰经" (MASAQ) 数据集为"古兰经"阿拉伯语提供了详细的语言注释,为这种古典语言推进了自然语言处理 (NLP) 研究和工具.

关键词:
语法注释语法注释分析 分析 分析阿拉伯语的 伊拉布 (ʾirāb)形态学注释 形态学注释语义关系是语义关系.语法关系是语法关系.标签 标签 设置 标签

更多相关视频

A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.4K
Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis
06:31

Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis

Published on: October 6, 2023

2.0K

相关实验视频

Last Updated: Jun 1, 2025

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms
05:12

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

674
A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.4K
Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis
06:31

Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis

Published on: October 6, 2023

2.0K

科学领域:

  • 计算语言学 计算语言学
  • 数字人文学科 数字人文学科
  • 语料库语言学 语料库语言学

背景情况:

  • 古典阿拉伯语,特别是"古兰经",对自然语言处理 (NLP) 具有显著的语言复杂性.
  • 对于"古兰经"阿拉伯语而言,有着显著的综合性,注释性体的稀缺性,阻碍了NLP模型的开发.
  • 现有的资源可能缺乏用于高级语言分析所需的详细形态和语法信息.

研究的目的:

  • 介绍"形态分析和语法注释的古兰经" (MASAQ) 数据集,这是"古兰经"阿拉伯语的新资源.
  • 为弥补注释的"古兰经"体的缺口,并支持创建复杂的NLP应用程序.
  • 为研究阿拉伯语NLP提供高质量,语言丰富的数据集.

主要方法:

  • 开发一个全面的数据集,注释整个古兰经文本,并提供详细的形态和语法信息.
  • 利用Tanzil.net.net的经过严格验证的古兰经文本.
  • 专家阿拉伯语言学家采用传统的I'rab方法来准确注释,结果超过131K个形态条目和123K个语法函数实例.
  • 在多种格式 (TSV,SQLite3,CSV,JSON) 中对数据集进行了结构化,以提高可访问性.

主要成果:

  • 该MASAQ数据集包括超过131,000个形态条目和123,000个语法函数实例.
  • 具有72个语法角色的全面标签集,详细的形态分析和特定上下文的注释.
  • 数据集以各种格式提供,方便各种研究应用.

结论:

  • 马萨克显著提高了为NLP研究提供注释的古兰经阿拉伯语数据的可用性.
  • 该数据集准备好推进阿拉伯语NLP任务,例如依赖性解析,机器翻译和语法检查.
  • MASAQ为NLP开发和阿拉伯语言学研究提供了宝贵的资源,促进了更准确的语言处理工具.