论文标题
uld@nuig在semeval-2020任务9：具有指数混合文本中情感分析的引起注意模型的生成词法
ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text
论文作者
论文摘要
在多语言社会中，代码混合是一种常见现象，由于各种原因，人们从一种语言转换为另一种语言。在不同的社交媒体网站上，公共沟通的最新进展导致书面语言中代码混合使用的频率的增加。在本文中，我们以注意力（GENMA）模型分析系统介绍了生成词，这有助于Semeval 2020 Task 9 Sentimix。该系统旨在预测给定的英语印地语代码混合推文的情感，而无需使用单词级语言标签，而是使用形态模型自动推断出来。该系统基于一种新颖的深神经网络（DNN）体系结构，该体系结构的表现优于测试数据集和验证数据集的基线F1得分。我们的结果可以在“ Sentimix印地语英语”页面上的用户名“ koustava”下找到
Code mixing is a common phenomena in multilingual societies where people switch from one language to another for various reasons. Recent advances in public communication over different social media sites have led to an increase in the frequency of code-mixed usage in written language. In this paper, we present the Generative Morphemes with Attention (GenMA) Model sentiment analysis system contributed to SemEval 2020 Task 9 SentiMix. The system aims to predict the sentiments of the given English-Hindi code-mixed tweets without using word-level language tags instead inferring this automatically using a morphological model. The system is based on a novel deep neural network (DNN) architecture, which has outperformed the baseline F1-score on the test data-set as well as the validation data-set. Our results can be found under the user name "koustava" on the "Sentimix Hindi English" page
