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There is no easy language or difficult language. There is a language that is properly studied and cared for, and a language that has not received sufficient study and care.

Choosing the structure of linguistic resources and identifying the appropriate tags to solve the problem is not a simple process, and it depends on the understanding of the problem and the way in which it is approached.

There is a great need for language resources that are built by specialized linguists, and are properly reviewed. There is also a need to coordinate efforts between the different entities in building language resources.

For machine learning to succeed, it is not enough to have huge data. Data also needs to be of high quality and designed in a way that approaches the problem properly.

Despite the common features between languages, each language has its own characteristics, which makes the tendency to copy a computer solution from one language to another a process doomed to failure.