If you have ever tried to find a particular piece of information by browsing through a large dataset, you undoubtedly understand how tedious and burdensome that task is. Sometimes, it can become a more manageable task when looking for a keyword or a combination of keywords. However, the work gets more challenging once you search for information and context without precise search indicators (such as keywords).
Suppose you wish to create a summary of a long text or get some information about hot topics that are important to you and your customers. How will you proceed to extract what you're strictly looking for when all you have is a question:
what is relevant data for my company at the moment?
Your question makes sense to a human being, but it needs to be translated for a computer to understand it. NLP is the translator. Thanks to NLP models, the computer will then proceed to extract accurately the data you need.
Currently, we can distinguish five popular NLP techniques that help in data extraction:
Named Entity Recognition is the most appropriate for short data excerpts such as dates, persons, or addresses.
Sentiment Analysis - still in development technique which may help you identify someone's mood or level of satisfaction.
Text Summarisation.
Aspects Mining applies when you wish to catch a context.
Topic Modelling is the most promising yet the least developed technique that may find links between somehow related topics.
Natural Language Processing is variously used and understands many languages. The most sophisticated models can understand slang and correct mistakes or errors made by humans.
However, the effectiveness and accuracy of the model will depend on the time spent on training and supervising the model - with some exceptions, e.g. topic modelling. The more effort you put into teaching your model, the more it will provide 'rich' and valuable data, personalized and tied to your needs.
Many more applications and use cases are possible with NLP and said techniques. For all business sectors, from legal to manufacturing. The fast development of Machine Learning and NLP has led to more effective automation and data extraction and decreased the cost of document digitisation.
Your imagination is the only limit.