Even if this is the first time you have ever come across the term NLP, you have already used or are using NLP applications. Devices or applications commonly used, such as GPS, voice assistants, chatbots, or speech-to-text dictation software, all have one thing in common. All of them use natural language processing.
Natural Language Processing or NLP aims to build machines capable of understanding text or voice data and processing responses for the same. The scope of NLP applications, however, has increased multifold. Today, NLP plays a role in business solutions, healthcare, surveys, targeted advertising, etc.
Let us dive deeper and understand the scope and use of NLP applications in detail.
Table of Contents
What Is NLP?
NLP or Natural Language Processing is the branch of Artificial Intelligence primarily concerned with developing machine learning. It enables machines to understand data in text or voice, similar to humans.
NLP is not limited to understanding data but gaining the ability to give out responses. By combining computational linguistics with statistics, machine learning, and deep learning models, computers can grasp the complete meaning with the intent and sentiment of the speaker or writer.
General Applications of NLP
NLP has emerged as the driving factor behind machine intelligence and learning for various real-world applications. Language is our intuitive behaviour to convey information, and it includes semantic cues such as signs, words, or images.
We can now apply the natural human response system to a machine with the help of machine learning and AI.
E-mail Filtering And Classification
NLP applications help to filter out spam and phishing e-mails by uncovering certain words or phrases. Indicators such as grammatical mistakes, inappropriate urgency, misspelt names, and more help NLP classify e-mails into various categories. Gmail’s email classification recognises three categories: Primary, Social, and Promotions.
Effective translation goes way beyond replacing words with another language. It has to capture the meaning and essence of the sentences to present a true picture. As the NLP technology has improved, so has the machine translation accuracy.
Grammar checks are a widely used NLP application to write better content. It offers the user various tools to correct grammar and spellings, improve digestibility, suggest synonyms, and deliver clarity.
Social Media Monitoring
Social media has become the everyday workplace for businesses and customers. NLP can analyse the unstructured data from social media and process it to create actionable insights for your business.
With the help of NLP applications, recruiters can go through countless resumes and shortlist candidates based on their requirements. NLP uses information extraction with named entity recognition to identify desired skills, education, and location.
Smart Assistant And Chatbots
Smart assistants such as Siri(Apple) and Alexa(Amazon) use voice recognition to understand speech patterns and respond with the solution. Similar to smart assistants, chatbots use the same feature to understand text information and respond accordingly.
Autocorrect and autocomplete are desirable features of NLP applications commonly used in our smartphones. Predictive text customises itself as it learns about your language quirks the more you use it.
NLP Applications In The Healthcare Sector
1. De Identification Model Under HIPAA
Under the Health Insurance Portability and Accountability Act, it is mandatory to protect sensitive patient health information. By deploying the NLP application, sensitive information such as name, address, and contact number are replaced with semantic tags.
2. Root Cause Analysis
NLP applications can go through vast caches of medical records to identify subsets of ethnic groups and population segments that face specific health problems. It allows physicians to understand the root cause of the disease.
3. Clinical Assertion Model
NLP applications enable healthcare providers to analyse clinical notes to identify the problem and severity of the patient’s condition. It also provides predictive analysis of the condition to diagnose and treat patients.
ICD-10 Clinical modification is a code used to extract information about medical conditions. Physicians can monitor the statistics to understand the outcome, health implications and side effects to design a better treatment.
How Does NLP Help Your Organisation?
NLP offers a lot of clear advantages for an organisation in Artificial Intelligence. Klearstack optimises your organisation’s functions for better growth and reduces overall costs.
Still wondering if your business needs NLP applications or not. Here are the key features of NLP your business can benefit from
NLP helps process unstructured data from e-mails, documents, research, reviews and many more. Large amounts of text-based information can be analysed and processed with improved accuracy.
NLP applications streamline manual processes to reduce the human effort spent on repetitive tasks. Organisations can reduce inefficiencies by deploying NLP features to search, analyse and process information.
Enhance Customer Experience
One of the critical benefits of NLP is that it strives to understand and recognise sentiment in the user messages. By training chatbots using industry-specific algorithms, one can enhance the user experience by responding quickly and correctly.
Organisations can achieve true potential and serve their customers well with better information. NLP applications collect data from various sources such as social media, reviews, e-mails, documents, etc. and convert them into actionable insights.
NLP applications reduces the workload of employees by performing repetitive functions. It enables the workforce to use their talents better and perform high-level tasks to boost business productivity.
NLP applications strive to understand the ambiguities in human languages to determine the intended meaning of text or voice data accurately. As AI and NLP technologies improve, so will the tasks such as speech recognition, speech tagging, disambiguation, entity recognition, sentiment analysis and language generation.