We’re officially a part of a digitally dominated world where our lives revolve around technology and its innovations. Each second the world produces an incomprehensible amount of data, a majority of which is unstructured. And ever since Big Data and Data Science have started gaining traction both in the IT and business domains, it has become crucial to making sense of this vast trove of raw, unstructured data to foster data-driven decisions and innovations. But how exactly are we able to give coherence to the unstructured data?
The answer is simple – through Natural Language Processing (NLP).
Natural Language Processing (NLP)
In simple terms, NLP refers to the ability of computers to understand human speech or text as it is spoken or written. In a more comprehensive way, natural language processing can be defined as a branch of Artificial Intelligence that enables computers to grasp, understand, interpret, and also manipulate the ways in which computers interact with humans and human languages. It draws inspiration both from computational linguistics and computer science to bridge the gap that exists between human language and a computer’s understanding.
The concept of natural language processing isn’t new – nearly seventy years ago, computer programmers made use of ‘punch cards’ to communicate with the computers. Now, however, we have smart personal assistants like Siri and Alexa with whom we can easily communicate in human terms. For instance, if you ask Siri, “Hey, Siri, play me the song Careless Whisper”, Siri will be quick to respond to you with an “Okay” or “Sure” and play the song for you! How cool is that?
Nope, it is not magic! It is solely possible because of NLP powered by AI, ML, and Deep Learning technologies. Let’s break it down for you – as you speak into your device, it becomes activated. Once activated, it executes a specific action to process your speech and understand it. Then, very cleverly, it responds to you with a well-articulated reply in a human-like voice. And the most impressive thing is that all of this is done in less than five seconds!
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Career Opportunities in Natural Language Processing
As we mentioned above, natural language processing allows computers to interact with humans in their own language. Through NLP, computers can hear speech and read a text, and simultaneously interpret and measure the sentiment behind it to respond accordingly. Since Big Data is being leveraged by most of the companies around the globe, organizations and institutions across the various sectors of the industry are resorting to NLP techniques and tools to extract meaningful information from massive datasets. Natural Language Toolkit (NLTK), Stanford NLP, MALLET, and Apache OpenNLP are some of the popular open-source NLP libraries used in real-world cases and applications.
The rising interest in the field of natural language processing is creating new career opportunities for professionals specializing in Data Science, Machine Learning, and Computational Linguistics. Reputed organizations like Facebook, Google, Sony Ericsson, British Airways, J.P. Morgan, Forte Group, Ernst & Young, American Express, Merrill Lynch, Shell, Celtic, and Sainsbury, to name a few, hire natural language processing experts and analysts.
The job roles in NLP are quite varied and branched out such as NLP engineer, NLP scientist, NLP architect, Voice Over Artist, NLP applied research scientist, cognitive data scientist, and so on. Apart from these roles, one of the most prominent job roles in the field of natural language processing is that of a Coach. Numerous companies hire NLP experts for the purpose of executive performance coaching in their respective institutions.
The salaries of NLP professionals are pretty decent. For instance, the average salary of a Machine Learning NLP engineer in the US ranges anywhere between $119,256 – $169,853 per year. An NLP Research Scientist, on the other hand, makes around $72,040 per year.
Why is NLP important?
The NLP helps in processing large-scale data. It enables computers to communicate with humans in the language which both know. For example, NLP allows computers to read, hear speech, interact and interpret important information.
Another important factor of NLP is the structuring of highly complex and unstructured data. Not only there are hundreds of languages and dialects, but within each language there consists a unique set of rules of grammar and syntax, slang, and terms.
Natural language processing in AI is used especially for human language with the help of supervised and unsupervised learning. The NLP helps to resolve ambiguities in language and adds numeric ability and structure to the data.
Natural Language Processing Tokenization
Tokenisation is nothing but a simple process which utilises the raw data and turns it into a useful data string. Although it is of great importance in the world of cybersecurity and NFT creations but it serves a big deal of importance in the NLP as well. It is used in NLP for splitting paragraphs and sentences into much smaller units.
Natural Language Processing in the Real World
The real-life examples of Natural Language Processing are –
- Email filters,
- Smart Assistants,
- Search results,
- Language Transaltion,
- Digital calls,
- Data analysis,
- Text analytics
Today, natural language processing is primarily used for text mining, machine translation, and automated question answering. In fact, NLP has found its applications in numerous real-world use cases including automatic text summarization, parts-of-speech tagging, topic extraction, sentiment analysis, named entity recognition, relationship extraction, stemming, and much more.
Here’s how natural language processing is being leveraged by companies across the myriad parallels of the industry:
The “Spell Check” feature of Microsoft Word is one of the most basic applications of NLP. then again, NLP techniques are in full swing in popular search engines namely Google and Bing. These search engines leverage NLP techniques to identify and extract keywords from text to parse search queries and populate search indexes on their site.
Businesses are using the NLP technique, sentiment analysis, to understand and interpret how their clients are reacting to their products and services. By uncovering the emotional outlook and response of the customers, sentiment analysis allows companies to enhance their products and services according to the taste and preferences of their customers.
The Royal Bank of Scotland has been one of the biggest proponents of Text Analysis. Using text analytics, the bank has been able to unravel important patterns and trends by diving into the customer feedback data from emails, surveys, as well as complaint calls. By analyzing and interpreting this data through text analytics, the bank is able to understand the grievances of its customers and improve upon them.
In the financial sector, companies apply NLP techniques to extract meaningful and relevant information from plain texts and using the data thus obtained, they can carve out data-driven trading decisions and strategies.
While these are basically text-based NLP techniques and applications, natural language processing has also extended to voice and speech recognition. Like we mentioned at the beginning of this post, NLP is used in smart personal assistants such as Apple’s Siri, Microsoft’s Cortana, and Amazon’s Alexa. These virtual assistants can perform all kinds of tasks – from simple tasks like changing the lighting of your room and providing weather updates to more complicated ones like shopping online for you.
Skills Required to become an ML and NLP Expert
Since natural language processing bridges the two worlds of linguistics and computers, it demands a certain degree of expertise in both the fields.
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You need to be able to understand the basic aspects and concepts of linguistics like speech recognition, information extraction, sentence fragmentation, parts of speech, and so on.
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ML NLP engineers or NLP research scientists must possess good programming skills. You should be well-versed in at least one programming language, be it Python or Java or Ruby, or any other high-level language for that matter. Also, you should possess the fundamental ML (classification, regression, probability estimation, data integration, decision trees, etc.) and NLP (syntax, semantics, speech recognition, etc.) concepts.
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Apart from these skills, you need to have a basic knowledge of Probability & Statistics and recursive neural networking (RNN). These are the essential components of many research fields and NLP is no exception.
As AI and ML technologies continue to progress, it is giving rise to new and exciting job prospects in the natural language processing sphere. In 2016, natural language processing featured as the hottest skill in the global jobs market on Upwork. This shows that the demand for skilled and trained professionals who can juggle both computer programming and natural language processing skills is bound to rise considerably in the near future.