Por Marcelo Inuzuka e Ruan Rodrigues.

For sentiment analysis in medium to low-resourced languages, a multilingual approach that resorts to machine translation can be competitive with standard approaches to the task. We develop a zero-shot hashtag segmentation framework and demonstrate how it can be used to improve the accuracy of multilingual sentiment analysis pipelines. Besides, also being useful for accelerating data annotation, our zero-shot framework establishes a new state-of-the-art for hashtag segmentation datasets, surpassing even previous approaches that relied on feature engineering and language models trained on in-domain data.

Bio:

• Marcelo is a faculty member of Federal University of Goiás, Brazil, and he is currently doing research on Argument Mining as a doctoral student at the NLX Group of the Dept of Informatics, Faculty of Sciences of the University of Lisbon.
• Ruan is a Machine Learning Engineer with experience in projects and challenges in the field of Natural Language Processing both in academia and industry. He holds a Computer Science degree from the Federal University of Goiás and is currently a student at the Erasmus Mundus European Masters Program in Language and Communication Technologies.

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