Social media has become one of the most popular sources of information. People communicate with each other and share their ideas, commenting on global issues and events in a multilingual environment. While social media has been popular for several years, recently, it has given an exponential rise in online data volumes because of the increasing popularity of local languages on the web. This allows researchers of the NLP community to exploit the richness of different languages while overcoming the challenges posed by these languages. Urdu is also one of the most used local languages being used on social media. In this paper, we presented the first-ever event detection approach for Urdu language text. Multiclass event classification is performed by popular deep learning (DL) models, i.e.,Convolution Neural Network (CNN), Recurrence Neural Network (RNN), and Deep Neural Network (DNN). The one-hot-encoding, word embedding, and term-frequency inverse document frequency- (TF-IDF-) based feature vectors are used to evaluate the Deep Learning(DL) models. The dataset that is used for experimental work consists of more than 0.15 million (103965) labeled sentences. DNN classifier has achieved a promising accuracy of 84% in extracting and classifying the events in the Urdu language script.

中文翻译：

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来自文本的多类事件分类

社交媒体已成为最受欢迎的信息来源之一。人们彼此交流并分享他们的想法，并在多语言环境中评论全球问题和事件。尽管社交媒体已经流行了好几年，但近来，由于本地语言在网络上的日益普及，它使在线数据量呈指数增长。这使NLP社区的研究人员能够利用各种语言的丰富性，同时克服这些语言带来的挑战。乌尔都语也是社交媒体上使用最广泛的本地语言之一。在本文中，我们介绍了有史以来第一个针对乌尔都语文本的事件检测方法。多类事件分类是通过流行的深度学习（DL）模型（即卷积神经网络（CNN），递归神经网络（RNN）和深度神经网络（DNN）。基于单次热编码，单词嵌入和术语频率逆文档频率（TF-IDF-）的特征向量用于评估深度学习（DL）模型。用于实验工作的数据集包含超过15万（103965）个带标签的句子。在用乌尔都语语言脚本提取和分类事件时，DNN分类器已达到84％的有希望的准确性。