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A Deep Learning Study On Understanding Banglish and Abbreviated Words Used in Social Media

机译:了解社交媒体中英语和缩写词的深入学习研究

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Day by Day, the trend of using social media(SM) has increased among the people. With the increasing rate, the use of Banglish(Merge of Bangla and English) and shortcut words has also being increased. In this research, Banglish and Shortcut words have been fully converted to English. For this type of conversation, we have used the CNNs method and the method consist of multiple layer and these layers are connected to each other. This method is considered to be the best method because it does not need any feature extraction. The convolutional neuron network is used in many areas such as image and pattern recognition, speech recognition, natural language processing and video analysis. In this research we have used CNN method because we have decided to use computer vision as some of the words are close to each other and at first we need to convert all the shortcut words into images for pre-processing the data. With the help of CNN method searched for the Banglish and shortcut words that people use for their daily conversation. At first we find the different representative of a single word and then we converted those shortcut and Banglish words to main word using CNNs method.
机译:日复一日,人民中使用社交媒体(SM)的趋势。随着速度的增加,伯文(Bangla和英语的合并)和快捷词也有所增加。在这项研究中,Banglish和快捷词已经完全转换为英语。对于这种类型的对话,我们使用了CNNS方法,并且该方法由多层组成,并且这些层彼此连接。该方法被认为是最好的方法,因为它不需要任何特征提取。卷积神经元网络用于许多领域,例如图像和模式识别,语音识别,自然语言处理和视频分析。在本研究中,我们使用了CNN方法,因为我们已经决定使用计算机愿望,因为一些单词彼此接近,并且首先我们需要将所有快捷单词转换为图像以预处理数据。在CNN方法的帮助下,搜索了人们使用日常对话的伯文和快捷词。首先,我们发现单个单词的不同代表,然后我们使用CNNS方法将这些快捷方式和张毛片单词转换为主词。

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