Guide to Natural Language Processing (NLP) and Its Use Cases

Natural Language Processing and its use cases

What is Natural Language Processing (NLP)?

The advancement of science and technology lead to the development of Artificial Intelligence. AI enables machines to think and make human-like decisions. A branch of Artificial Intelligence is natural language processing (NLP). Natural language processing, in simpler terms, enables conversations between a computer and human in human speakable languages.

Machine learning acts as a backbone to NLP in helping the machine to read, decipher and understand human languages. NLP makes use of high-level algorithms to apply natural language rules to interpret the exact meaning of a word or even an entire sentence. The collected data is then converted into a format that can be understood by the machine. Two main technologies namely, syntax analysis and semantic analysis, together make up the NLP. Syntax analysis helps in arranging the sentence in a grammatically correct format whilst semantics analysis deals with conveying the meaning of a sentence. NLP has reached a new milestone in the field of technology. But sometimes, the complexity of the language structure might make it difficult for the machine to exactly understand the context or the meaning of a word or sentence.

Current adoption trends of NLP

Natural language processing has found its way through many applications across different industries. For instance, it is very common to find NLP-powered chatbots on various websites. These NLP-powered bots provide answers to basic customer queries and offer support in the areas possible. In need of advanced solutions, these bots then connect the client with a support agent. These NLP-based bots have been greatly helpful in maintaining a good customer relationship while also making it easy for the support agents to seamlessly manage queries.

Another tremendous contribution of NLP lies in the development of autonomous vehicles. Companies like Tesla have been developing driverless cars and trucks. The natural language processing technology in autonomous vehicles enables the vehicle to understand and act on user-provided inputs like the destination point. Many other vehicles also have inbuilt SatNav that uses NLP to locate the places based on the driver’s audio input.

Future of Natural Language Processing (NLP) in the next 5 years

Natural language processing has already been hugely helping companies in providing insights about how the customers feel about their products and services. It is no wonder that NLP would soon become more mainstream shortly. Trillion-dollar companies like Google are rapidly investing in bringing AI and NLP to behave and respond exactly like human beings. For example, a new technology called MUM or Multitask Unified Model was released by Google in the 2021 Google IO event. This Multitask Unified Model is said to be 1000 times more efficient than Google’s search engine and has been trained in over 75 languages.  Depending on the user’s query, MUM can provide relevant information from all over the internet.

From the above example, it is evident that AI and NLP would soon be able to function as efficiently as human thinking capabilities. Also, machines would be capable of self-learning by accessing data from the internet. This would eventually lead to the increased adoption of NLP across various business verticals including startups and SMEs.

Market Size and growth rate of natural language processing (NLP)

The global natural language processing (NLP) market is estimated to reach over $35.1 billion by 2026 with a CAGR of 20.3% between 2020 to 2026. After intense research, a conclusive report shows that a major contribution towards the growth of NLP will be because of the healthcare, life sciences, retail and eCommerce industries.

NLP services are classified into two categories as cloud-based and on-premise. Companies that prefer scalability, availability and aim on cost-saving prefer cloud-based services. The companies that consider data as their most important asset and need high-level security often prefer on-premise services. Among the two types of NLP services, the on-premise method is predicted to be adopted more than the cloud-based NLP services.

Major players in the field of Natural Language Processing (NLP)

1) Microsoft

Microsoft’s language understanding (LUIS) enables developers to create language modules that are more specific to their use-cases. LUIS also enables developers to build natural language understanding modules without any prior ML experiences. LUIS meets international compliances like ISO, HIPPA, SOC, etc while being able to be developed as cloud or on-premise tools.

2) Google

Google is one of the top NLP service providers. Google offers cloud-based AutoML Natural Language and Natural Language API services for enterprises for gathering practical insights on customer’s thoughts about their products and services. Other than this, almost all of us have come across the infamous google assistant that simplifies almost all of the essential tasks for us. Starting from setting an alarm to making a hotel reservation, Google assistant is capable of various things.

3) Amazon

Amazon’s Comprehend is a powerful natural language processing service. Comprehend can analyze, understand and gather information from unstructured data sets. Comprehend can identify important elements of the data including language references, people and places. Comprehend can also be specifically tailored to identify certain terms depending on the needs. This fully managed NLP service eliminates the need to train models from scratch. It can effectively analyze a huge number of articles and documents with the help of advanced machine learning algorithms.

Limitations of natural language processing

Although natural language processing has brought enormous benefits into the technology sector by playing a major role in providing insights, analysis, etc, there are some challenges associated with NLP services that are yet to be addressed. Some of the most common challenges where NLP cannot interpret the meaning are as follows.

  • Complex words or biblical words
  • Contextual words and uncommon phrases
  • Sentences with Irony or Sarcasm
  • Word pronunciations that differ because of accents
  • Programming languages and domain-specific languages

Career scope in natural language processing and available courses 

The adoption of natural language processing services keeps increasing gradually and in the upcoming years, the momentum would only keep increasing due to the enormous benefits that NLP offers to businesses and individuals.

The field of AI, ML and NLP together offer various career opportunities to people with prior knowledge in the field of computer science, mathematics and statistics. Students and professionals who are interested in NLP can take advantage of online  NLP courses. Some of the best ones are mentioned below.

  1. Natural language processing with python – Udemy
  2. Advanced machine learning and NLP specialization – Coursera
  3. Data Science: natural language processing – Udemy
  4. Getting started with NLP in python – Pluralsight
  5. Natural language processing and language understanding – edX

Natural language processing and Natural language understanding are technologies that are very much beginning stages. Even at such early stages, these technologies offer so many benefits to businesses and enterprises regardless of their domain and size. In the future, the advancement of NLP services could potentially bring staggering changes to the business world.

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