安科利克-内尔:孟加拉地区命名实体认可的基准数据集
Bidyarthi Paul1, Faika Fairuj Preotee1, Shuvashis Sarker1
1Department of CSE, Southeast University, Dhaka, Bangladesh.
PloS one
|February 25, 2026
概括
本研究介绍了ANCHOLIK-NER,这是孟加拉地区方言中命名实体识别 (NER) 的第一个数据集. 孟加拉语BERT表现强,突出了对方言意识NLP模型的需求.
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.
- 低资源的语言技术
背景情况:
- 区域孟加拉语方言的命名实体识别 (NER) 尚未得到充分研究.
- 现有的NLP模型与巴里沙尔,奇塔贡,米门辛格,诺哈利和西尔赫特等方言的独特语言特征作斗争.
- 缺乏专门的数据集和基准标准阻碍了该领域的研究.
研究的目的:
- 介绍ANCHOLIK-NER,这是孟加拉地区方言中NER的第一个基准数据集.
- 在本数据集中为基于变压器的模型提供基线性能指标.
- 为低资源语言促进对方言意识NLP的未来研究.
主要方法:
- 创建了ANCHOLIK-NER数据集:17,405个句子,101,817个单词,10个跨越5个地区的实体标签.
- 来自公共资源的数据来源和用于实体调整的手册翻译.
- 评估了三个变压器模型:孟加拉伯特,孟加拉伯特基础和伯特基础多语种套件.
主要成果:
- 孟加拉语BERT在各方言中获得了最高的F1分数:Mymensingh (82.27%),Barishal (81.48%),Sylhet (78.75%),Noakhali (78.50%),奇塔贡 (75.31%).
- 在Mymensingh和Barishal方言中表现强.
- 鉴定了奇塔贡方言由于显著的变化而更具挑战性.
结论:
- ANCHOLIK-NER是孟加拉语方言NER的一个基础资源.
- 孟加拉语BERT提供了有希望的表现,但方言特定的挑战仍然存在.
- 未来的工作应该专注于方言意识的适应和扩大数据集覆盖范围.
相关概念视频
01:12Regional Terms
16.5K
Regional terms describe anatomy by dividing the body parts into different regions that contain structures involved in contributing similar functions. Using these terms helps increase the accurate description and identification of the particular region of interest or region affected by the disease.
Primarily, the human body has two major regions, the axial and appendicular regions. The axial region comprises regions from the head to the abdomen and makes up the central body axis. In contrast,...
Primarily, the human body has two major regions, the axial and appendicular regions. The axial region comprises regions from the head to the abdomen and makes up the central body axis. In contrast,...
16.5K
01:29Aggregates Classification
1.1K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.1K