Natural Language Processing and Artificial Intellig

Original article was published on Artificial Intelligence on Medium

Natural Language Processing and Artificial Intelligence

Note that this is a shortened version of our recent blog post which you can find here.

What is Natural Language Processing?

Natural Language Processing is the process of converting phrases or sentences in natural language into algebraic structures. In NLP, machine learning algorithms assess a huge amount of human-produced text and study the context of dialogue, prose, and data. These algorithms are trained to understand what is being read and pick out different meanings and intentions.

Step one: Lexical Analysis

Step two: Syntactic Analysis, or Parsing

Step three: Semantic Analysis

Step four: Discourse Integration

Step five: Pragmatic Analysis

These five steps are key for businesses and services who are looking to launch new projects that leverage text data in order to validate, improve, or expand their offering to customers.

Where is Natural Language Processing and AI used?

Here are a few of the common real-world examples where these two fields overlap to provide value to users.

Example one: Spell checking

Most people don’t write grammatically correct sentences when texting, and yet, thanks to AI, your device still has a great level of understanding and can support the intentions of your message, based on how you usually write and what you like to talk about.

Example two: Smart searching

Some of the top e-commerce stores, like Amazon, are applying smart searching technologies to the search bar on their sites. This combination of NLP and AI provides a very valuable function, as it not only changes its drop-down recommendations with each letter that you type, but it also presents suggestions linked to your previous purchases.

Example three: Translation

Whilst human translators are expensive, most online tools are free and some of the elite ones use NLP and AI to learn what sort of topics are being talked about so that the translation results can become more accurate. These tools massively aid globalisation.

Continue reading as we go into greater detail about:

  • Where NLP and AI can be applied
  • Some of the well-known examples of NLP and AI
  • The limitations of computing for NLP
  • What pipelines are and how they are used

Why are NLP and AI good for business?

Just imagine using the internet, but without Google search. That’s one clear cut example of what NLP/AI is used for. Here are a couple more:

  • Chatbots — building an intelligent and interactive bridge between a business and their customers, one that learns and improves over time
  • Human understanding — as NLP and AI improve their knowledge of human behaviour, their services improve and make products and customer experiences smoother and more enjoyable

Here at CryptoMood we are building something incredible at the exciting intersection of natural language processing and artificial intelligence. We are using our technology to analyse the sentiment of articles and the emotions of user content, so that we can provide cryptocurrency traders with sentiment scores that can support their trading decisions.

What do Natural Language Processing Researchers do?

CryptoMood HQ houses an impressive team of NLP researchers with strong academic and practical backgrounds. In supporting our mission to provide the best sentiment analysis tool to the cryptocurrency world, they are working on a range of interesting tasks which can be grouped into the following five activities.

  • Searching for available datasets
  • Pre-processing data
  • Analysing data
  • Making models
  • Analysing results

Our researchers are hard at work with other fields too, such as machine learning, and optimisation theory. They read Jurafsky, Martin, and Goldberg. They pore over their mined data and create expansive and masterful pipelines. They keep us leading the way.