Text has always been a great source of data and information and has historically been a major way to share knowledge and ideas. This rich source of information can be extremely interesting for machine-learning systems. It's a prime example of so-called non-structured data.
Even though it's challenging, the field of machine learning for NLP is very important in that it can unlock major improvements in our lives.
These are some of the major sub-fields of NLP:
- Speech, such as speech recognition and text-to-speech, in which the given text will transform into a spoken representation.
- Automatic summarization, the task of producing a summary of a long text that maintains its meaning and is readable.
- Semantics, the branch that wants to understand the meaning of words in a context. Common examples are machine translation and natural language generation...
