Natural Language process (NLP) could be a specialized kind of machine learning that's tailored for text. Since humans work with text, typically during a verbal type, it's a decent downside domain for neural networks. NLP coaching model is actually a neural network that has been trained to handle specific issues with a particular kind of data.
In the future, NLP can modify additional automated responses and a stronger understanding of human conversations. Commands to our phones and computers are going to be handled with the next degree of sophistication. Improvements to sentiment analysis can come back from NLP neural network enhancements and higher data. Typically unnoticed is that the quality of data. Selecting the correct neural network for the correct downside could be a challenge. It is important to use the right kind of neural network for the matter at hand.
What are the challenges of NLP?
Challenges can be found at multiple levels. The neural network architecture, the number of levels and how these layers are interconnected, continues to evolve. One challenge is to design better neural network frameworks.
Selecting the right neural network for the right problem is another challenge. The old saying about using the right hammer for the right job fits well here. We don't want to use a sledgehammer to hang a picture on the wall. Likewise, it is important to use the correct type of neural network for the problem at hand.
The model trained is only as good as the data. The data needs to be comprehensive, correct, and relatively free of bad data points. Preparing the data is often the most time-consuming and important part of the process.
Another important factor is the correct interpretation of the results. Sometimes the analysis output is represented by a set of numbers measuring different aspects of the results. If these are interpreted incorrectly, then the overall effort may be of less value than it might otherwise be.
What are the emerging trends happening now in NLP space?
It is a continually evolving game. We will see improvements in NLU/NLG which will give rise to new capabilities and applications. Personal assistants similar to Alexi and Ok Google will assist humans in all sorts of endeavors. More companies will be rolling out NLP applications that will often be "homegrown", that is, they will not be Amazon or Google based. Instead, they may well rely upon technology produced by other NLP vendors such as IBM.
Many NLP applications will incorporate hybrid approaches where analysis techniques are paired with human intervention to provide a more meaningful and satisfying response. When the NLP techniques reach their limits, a human will intervene. Handcrafted responses are currently being used for specific, limited problem domains. For example, personal assistants can only answer certain types of queries. The seemingly more capable ones are structured to handle a tightly defined set of interactions.
NLP processing will become more distributed. Both the training and data sets may be distributed across a variety of platforms. Smartphones and similar devices will have ML functionality built-in into them in the form of specialized processors. This again will usher in new uses for NLP technology. Data will come from a more diverse set of sources as sensors and actuators become more prevalent in society.