What is Natural Language Processing?

Humans speak to each other in a specific manner, that does not always adhere to the set boundaries and rules. While a ‘hmmmm’ might mean yes in one country, it might mean ‘no’ in another, just like nodding of head. Even when we use full sentences, words don’t always mean what they say. When I say “I know what you are saying”, I might actually know what you are saying, or maybe I am just being sarcastic. Is there any way to know which meaning I intend to convey? Maybe you can understand that by seeing my face. Or perhaps by understanding the entire conversation as a whole. Making a computer understand these nuances of human languages, be it written or spoken, is termed as Natural Language Processing.

How is it different from Machine Learning?

Both NLP and ML are subsets of Artificial Intelligence. While ML mainly focuses on finding the probability or the outcome of events based on certain factors, NLP deals only with languages and their understanding. The best example of NLP in action can be found in this video here. While ML has grown by leaps and bounds and is actually finding real world applications, NLP is still in a very nascent stage due to the fact that people speak very differently – every human might have some traits that they use while speaking, which might be specific to their age group/ethnicity/location, etc. For a computer to understand all of this and reply back in a way that it passes the turing test (such that people do not understand that it is a computer that is replying back), it needs massive amount of data to train on.

Finding datasets for Natural Language Processing

Different types of data might help research in the field of NLP in different manners. We were speaking only of the spoken and written words, but NLP has even helped scientists use sounds made by animals and birds to boost their conservation in many cases. The most common dataset for NLP would consist of written text or voice recordings and the computer can be trained in a trial and error method where it tries to interpret a text or recording and is directed by a human whenever it goes wrong.

Datasets for Natural Language Processing would be typically tough to gather since these generally will not be xlsx or csv sheets like ML. Most people start with NLP by MNIST dataset. Here you are provided handwritten text, and you need to train the computer to understand this text through multiple iterations of training and retraining.

How can DataStock help you get datasets for Natural Language Processing?

Getting datasets that are favorable for being used on NLP based models, is pretty difficult even if you can see the data online. Getting voice/written data from multiple sources and building a repository that has uniformity in itself is a uphill task that DataStock can solve for you. Cleaning and labelling the data and providing it in your desired format are things you can leave to DataStock as well.

So go on, get your datasets for Natural Language Processing and train your NLP models and make your business smarter by leveraging something that is there in abundance but takes time to mould into proper shapes – data.