What are the steps of Natural Language Processing?
Before we begin walking you through the steps of Natural Language Processing, it’s vital that we have an overview of what NLP actually is. To simplify, NLP is the process of converting phrases or sentences in natural language into algebraic structures.
In Natural Language Processing, machine learning algorithms assess a huge amount of human-produced text, studying the context of dialogue, prose, and data. These algorithms are trained to better understand what is being read and pick out different meanings and intentions.
NLP is a superset of Natural Language Understanding (NLU), which takes data and maps it into natural language formats. We will be covering NLU, as well as Natural Language Generation (NLG) within this article here.
Now that you have a good basis of the acronym itself, let’s provide a brief overview of the steps of Natural Language Processing, of which there are five.
Step one: Lexical Analysis
The first phase of NLP involves identifying and analysing the structure of words. The word ‘lexical’ refers to the vocabulary of a language, so by lexical analysis, this stage-specific relates to the words themselves and their structure by way of paragraphs and sentences.
Step two: Syntactic Analysis, or Parsing
This process looks at the grammar used in the text and the relationship between the words. For example, the sentence ‘The party go people to a’ would be systematically rejected by syntactic analysis.
Step three: Semantic Analysis
Meaningfulness is the name of the game during the semantic analysis stage. The text is checked and the dictionary definitions are applied, then the syntactic structures and objects are mapped. An example that would be rejected by a semantic analysis process is ‘soft stone’.
Step four: Discourse Integration
A story or article flows and builds information as it goes along, which is why the sentence you are reading holds meaning because of the sentence before it, and it will continue to provide meaning to the sentence that follows it. This is the basis of discourse integration, gaining meaning from groups of sentences, rather than seeing them as independent chunks of text.
Step five: Pragmatic Analysis
Real-world knowledge of the language, culture, and communication is applied during this stage, where what is being said is assessed for what is actually meaning meant.
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. However, there’s much more to this active field of research and development than meets the eye…
Where is Natural Language Processing and AI used?
Whilst the previous section looked specifically at what it is and how it works, this section will explore the multitude of applications for NLP and AI when their immense capabilities are combined. As we explore all of the unique and interesting ways in which they are used, we will also attempt to underline some of the computing limitations for NLP, explain what pipelines are, and we will also discuss some of the business considerations of the NLP/AI world.
Before we get to some of the more technical factors of NLP/AI, let us give you a few of the common real-world examples of where these two fields overlap to provide value to users.
Example one: Spell checking
As far as examples of NLP come, spellcheck is one of the most commonly used. It’s helpful, it speeds things up, and it reduces mistakes. 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. Over time, the search bar learns more about you to help you find the things you want to buy.
Example three: Translation
In an ever more connected world, it’s vital that we are able to communicate with each other, despite the natural language barriers that may exist. Markets that were previously closed and products that were once inaccessible can now be sought out with the help of translation technologies. 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. Lilt, Google Translate, and Yandex are some of the best examples you can find.
Why are NLP and AI good for business?
When we asked the question ‘what is NLP and AI good for?’ over a coffee recently, one of our developers had a great response, stating ‘just imagine using the internet, but without Google search’. That’s one clear cut example of what NLP/AI is used for. We know that when these two fields intersect, they can do wonderful things, and fortunately, businesses have not been slow to capitalise on this convergence.
This section will look at three particular areas of the unification of NLP and AI: the business benefits, the user benefits, and how CryptoMood is applying this technology.
Chatbots are one such example of businesses capitalising on NLP and AI. Each time you interact with an AI chatbot, you’re providing additional data to a machine learning system. When you click the search result, you confirm that the chatbot has done the right thing and provided the necessary information. When it comes to sales, having a live chatbot on your website is an amazing way of helping potential customers to reach the products that they want to buy. As well as saving money on sales staff, or at least on creating new sales funnels, they are also effective as customer support channels, helping people resolve their problems by learning the common issues and what steps must be taken to fix them.
On the business side, using NLP and AI to understand the intentions and behaviours of people is a way of doing better business. For customers, NLP and AI work best when they are making life easier, a different kind of productivity to that which businesses are seeking. Think of spell checkers, search engines, translation apps, spam filters, and chatbots – each of these is there to make digital life easier for general people. There are more advanced examples too, such as article summarization, sentiment analysis, and communication with voice assistants like Siri and Alexa.
Finally, what are we doing here at CryptoMood to build something incredible at this exciting place where natural language processing and artificial intelligence meet? Well, 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. We also use NLP as part of our spam filter, making sure that low quality and poorly written or inaccurate articles do not get considered for sentiment scoring.
What do Natural Language Processing Researchers do?
Here at CryptoMood, we have built 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
The job of a natural language processing researcher is not easy, they will face many challenges and limitations along the way. For example, our NLP team must work with neural networks, which are the universal approximators to any function. They know that if they have a lot of meaningful data available, and a neural network with excellent architecture and sufficient power, then things will work very capably. However, building and training the architecture is difficult, as is the data acquisition, creating hurdles that they must overcome.
Fortunately for our team, the field of NLP has come on leaps and bounds over the last decade, meaning the starting point is much further ahead, giving an advantage over our predecessors. Right now, the level at which computers can understand natural language is at a very high level. It’s only when very high-level actions come into play that there are problems for computers, for example, humans are able to reason with themselves and make connections between multiple fields of information and different sensory information to produce a decision. We also have imagination and memory and can adjust our thoughts as we write or speak. Some of these combined behaviours are too advanced for NLP systems.
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. Some of our researchers, when not working hard over processes and algorithms at CryptoMood HQ, are pursuing higher qualifications in their specialist fields of study, which we support. The more they know, the more powerful our tools can become.