Democratizing NLP: Considerations From Resources To Algorithms

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#Deep Learning #NLP #Natural Language Processing #IIT Hyderabad #Indian Institute of Technology #Indian Languages #Machine Learning #Artificial Intelligence

The talk addresses how Natural Language Processing (NLP) can be extended to low-resource languages, which so far have yet to attract enough NLP research. For more details about our events or to join our D-list, visit https://r6.ieee.org/scv-cs

In recent times, there has been an enormous increase in the use of digital media as a channel for communication. This, combined with the fact that natural language is a ubiquitous mode of communication, has increased the volume of natural language data available for processing. Also, this has resulted in opportunities for extending NLP-based services/solutions in multiple languages. Although existing algorithms for NLP tasks have been shown to achieve reasonable performances for high-resource languages such as English, French, etc., across tasks, the same is not true for many other languages widely spoken in the world. To ensure that the benefit of NLP research reaches a wider audience across different regions, it is important to pay special focus to the low-resource languages or consider support for low-resource languages in an intelligent manner in the algorithm design phase. The talk touches upon a few such considerations. 

For more details about our events or to join our D-list, visit https://r6.ieee.org/scv-cs

This talk addresses how Natural Language Processing (NLP) can be extended to low-resource languages, which so far have yet to attract enough NLP research. For more details about our events or to join our D-list, visit https://r6.ieee.org/scv-cs

In recent times, there has been an enormous increase in the use of digital media as a channel for communication. This...

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