Named Entity Recognition have been studied for different languages like
English, German, Spanish and many others but no study have focused on Nepali
language. In this paper we propose a neural based Nepali NER using latest
state-of-the-art architecture based on grapheme-level which doesn't require any
hand-crafted features and no data pre-processing. Our novel neural based model
gained relative improvement of 33% to 50% compared to feature based SVM model
and up to 10% improvement over state-of-the-art neural based model developed
for languages beside Nepali.

本文提出了一种基于神经网络的尼泊尔语命名实体识别模型，采用最新的基于字形的架构，无需手工制作特征和数据预处理，相对于基于特征的 SVM 模型以及针对尼泊尔语以外语言开发的最新神经模型，获得了 33% 至 50% 的相对改进和高达 10% 的改进。

尼泊尔语命名实体识别

Named Entity Recognition for Nepali Language

Indicators of Compromise (IOCs) are artifacts observed on a network or in an
operating system that can be utilized to indicate a computer intrusion and
detect cyber-attacks in an early stage. Thus, they exert an important role in
the field of cybersecurity. However, state-of-the-art IOCs detection systems
rely heavily on hand-crafted features with expert knowledge of cybersecurity,
and require a large amount of supervised training corpora to train an IOC
classifier. In this paper, we propose using a neural-based sequence labelling
model to identify IOCs automatically from reports on cybersecurity without
expert knowledge of cybersecurity. Our work is the first to apply an end-to-end
sequence labelling to the task in IOCs identification. By using an attention
mechanism and several token spelling features, we find that the proposed model
is capable of identifying the low frequency IOCs from long sentences contained
in cybersecurity reports. Experiments show that the proposed model outperforms
other sequence labelling models, achieving over 88% average F1-score.

本研究提出了一种基于神经网络的序列标注模型来自动识别网络入侵的迹象（IOCs），不需要专家级别的网络安全知识，通过对多个 token 拼写特征使用注意力机制，该模型可以从网络安全报告中识别出低频 IOCs，并取得了 88% 以上的平均 F1 分数，优于其他序列标注模型。