Natural Language Processing (NLP) is a major trend in the field of data science. Billion-dollar companies are allocating great sums of money to research this field. Not only multi-billion organizations, but even private individuals with websites, blogs, and other cloud-related businesses are also in the frontline to understanding NLP as well as its applications. Everyone wants to integrate this revolutionary technology into their business.
So, why are so many people adopting NLP techniques?
Numerous social media handles, digital platforms, and organizations can make use of abundant stores of digital information they have gathered in order to produce valuable products..
Although businesses have already been using this information, or rather, data for their needs, about 80% of the data is inaccessible and unstructured. This has brought about the need for NLP to resolve the issue.
NLP permits numerous organizations that need to analyze information or textual data fast and reliably.
NLP is nearly everywhere, although the reader might not have realized this. When you try to send an email, your email application will automatically correct you. If you miss the attachments that you referenced in the email text. These are a couple of the most common examples of NLP which you are likely to encounter during your everyday life.
This article will dig deep into other examples and applications of NLP.
Let’s dive in:
Applications of Natural Language Processing
Chatbots
Certainly, you have heard a lot about, or even had the chance to experience, chatbots. Nowadays, chatbots are the perfect solution for customers’ frustrations concerning technical support. Chatbots offer convenient virtual assistance for simple problems and also solve, or rather, offload, the low priority and high turnover tasks that do need skills. Moreover, there will be intelligent chatbots that will provide personalized assistance to consumers in the near future.
These standard questions and answers systems work under pre-defined rules where the Artificial Intelligence chatbot learns from each interaction and responds accordingly. Amazingly, they learn from these interactions and improve with time; this capability is termed machine learning.
These intelligently designed machines are becoming the trends at the frontline of customer service and assist the staff to solve a significant 80% of their routine queries. Unlike human operation, chatbots are available 24/7 and accelerate the response time thus improving customer service. Agents are relieved of the repetitive responses that have proven to be boring and time-consuming.
Sentiment Analysis
Sentiment Analysis under NLP is commonly used on social media and website monitoring. This is an ideal tool to understand and analyze the responses to business messages that are written and published on social media pages, or rather, platforms.
Natural language understanding is hard for machines when looking at human emotions and opinions. This difficulty is exacerbated because humans often use irony and sarcasm as a way of expressing their feelings. However, with sentiment analysis, it is possible to identify subtle nuances in opinion and emotions and figure out how negative or positive they are. This application is also called opinion mining.
Opinion mining is carried out by joining together statistics and NLP assigning values; positive, neutral, or negative to the chosen text. Then efforts are put to determine the mood of the context whether happy, angry, sad, annoyed among others.
This application is very useful in assisting business companies to gain insights on clients and perform a competitive comparison and, in turn, make the required adjustment in their business ideologies and strategies. This kind of data is also important in configuring an ideal customer experience as well as improving their products. Additionally, sentiment analysis is a great way to understand brand perception.
Market Intelligence
Any business market is influenced and also impacted by market know-how as well as information exchange between, stakeholders, companies, governments, and regulatory bodies. It is, therefore, very crucial to stay updated with the changing standards and industry trends. NLP is a perfect technology to monitor and track the market intelligence reports and then, obtain the important information to improve your business and come up with new strategies.
Analyzing sentiment, keywords, topics, and unstructured data can boost your market research, illuminating the trends as well as business opportunities. With this revolutionary technology, you can analyze online data and then identify customer pain points. With this, you will always be a step ahead of your competitors. Such an opportunity indicates that NLP is the key to the pursuance of Artificial Intelligence since language is the dictator of intelligence in society. Experts predict that NLP will be the key for every business to thrive in this competitive markets in a short while
Speech Recognition
Speech recognition technology has existed for over 70 years. The first speech technology recognition system was tested or rather introduced in 1952, by Bell Laboratories. This system was known as “Audrey” and could identify a single-digit number. Thereafter, IBM introduced “Shoebox” that could comprehend and respond to a 16-word sentence in English. With this, the market for the usage of NLP improved drastically.
Nowadays, the advances in NLP have begun using voice as a way of giving inputs to the system, instead of typing, clicking, or selecting a text. Today, voice assistants including Siri, Cortona, Amazon Alexa, and Google Assistance are some examples of how intelligent machines have been mastered to recognize the human voice and then understand the intent and respond accordingly. Surprisingly, NLP is the main technology behind the introduction of Voice User Interface. Another example to demonstrate speech recognition is your speech-to-text feature available on most smartphones. This is a default app in most smartphones, but other mobile applications provide this to users. They allow smartphone users to give audio inputs that are converted into text.
Text Classification
Text classification is typically a text analysis work that involves sentiment analysis. The task involves automatically understanding and processing and then categorizing your unstructured data.
Text classification algorithm plans the basis of online platform systems that process data and information on a large scale. For instance, email clients utilize text classification technology for tagging emails into different categories so that they can appear either in your inbox or your spam folder. Additionally, the automatic classification of emails either as social, primary promotions, or updates in your email software has been made possible by text classification used in NLP.
Another example; when you want to analyze numerous open-ended responses to your latest survey, it would take a lot of time if you do it manually and it will also be extremely expensive. What if you trained a NLP software to tag your data automatically according to the way you have customized it. You can use a topic classifier for your survey responses and categorize your data into topics such as Features, Customer Service, Pricing, and Ease of use. This is incredible!!
Text Prediction
Text prediction is a way of estimating the next word in a sentence or phrase. A popular example of text prediction is Google Search. Google utilizes Bidirectional Encoder Representations Transformers( BERT) which is a NLP using neural networks to form pre-trained models. The models are trained using numerous unannotated texts that are available on the web. BERT algorithms assist the search engine to comprehend queries in a design that is similar to humans. More applications that comprehend this strategy including Gmail, Google Docs, and compose using the NLP models to facilitate the text prediction.
Text Summarization
Automatic Text Summarization is self-explanatory. This is a process of summarizing your texts by extracting or obtaining the most significant information. Its main objective is to simplify the tiresome process of going through massive amounts of information and data such as news content, scientific papers as well as legal documentation.
Generally, there are two methods of applying NLP to make a summary of your data:
– Extraction-based summarization that extracts your keyphrases and then creates a summary, without using or adding any extra information.
– Abstraction-based summarization creates new phrases by paraphrasing your source. This approach is much better since you will create original content without losing value. This will save your precious time that is wasted as you read through an entire document to make the summary. Just key-in your words and let your chosen software work it for you.
Machine Translation
Machine translation is among the first applications of NLP- NLP. This is the process of translating a text or source language into another language, proving to be very useful. The process of machine translation can be understood by the following procedure:
Source Text- Deformating- Pre- editing- Morphological, syntax, semantics, contextual analysis- Internal representation of your source language- Contextual semantics and also syntactic generation- Reformating- Post-editing and finally Target Text.
There are various types of machine translations such as:
– Bilingual Machine Translation System: It gives translations between two specific languages
– Multilingual Machine Translation System: This system gives translation between any given pair of languages. The machines can be uni-directional or bi-directional.
The direct machine translation approach is less popular but at the same time, it is the oldest approach. The systems that utilize this approach can translate the source language directly to the target language. These systems are unidirectional and bi-lingual.
The systems that apply the Interlingua approach translate source language via an intermediate language known as Interlingua and then translates it to the target language.
Finally is the Empirical Machine Translation that uses massive amounts of raw data as parallel corpora. This raw data consists of texts together with their translations. Example-based, Analogy-based, and memory-based machine translations use the technique of empirical Machine translation approach.
Intent Classification
Intent Classification is designed to identify the goal or rather the purpose that is primary to your text. Other than chatbots, intent detection works to improve the benefits in sales as well as customer support areas.
By analyzing customer interactions such as chats, emails as well as social media posts, you can identify the customer that is willing or ready to make a purchase. The faster you identify and turn the leads into sales the more you grow and enhance your business. Some email classifiers enable you to sort the responses into categories such as interested, not interested and also, unsubscribe. Moreover, looking for customers’ intent in your customer service ticket as well as social media posts alerts you of customers at the risk of churn. This allows you to take immediate action and design a strategy to win them back.
Digital Phone Calls
You have heard of the phrase ” the call will be recorded for training purposes”, but did you understand what that entailed?. It turns out that the recordings might be used for training purposes in case a client is aggrieved, but most of the time, the recording goes into the server or NLP database to learn and improve or advance in the future. These automated machine systems direct all the customer calls to online chatbots or their service representatives who respond to client queries and come up with solutions. This practice has been embraced by numerous companies and organizations such as telecommunications providers. NLP also allows computer-generated language that is close to a human voice. It receives phone calls meant for scheduling appointments such as your haircut, and all this is automated.
Text Extraction
Information extraction or text extraction automatically highlights specific information in your text including companies, names places, and more. The process is also called “entity recognition”. This allows you to extract keywords within your text and pre-defined features like product models and serial numbers.
The application of information extraction includes accessing incoming support tickets and then identifying the specific data such as number orders, company names, and email addresses without having to read through all the tickets.
Text extraction can also be used for your data entry. It is possible to get the information you require and then set up a trigger that automatically enters the obtained information into your database.
Generally, keyword extraction gives you an overview of the text content. Together with sentiment analysis, this process can add a layer of insight by showing the words that clients use most often to express positivity or negativity towards your services or products.
Grammar Checkers
This is another commonly used application in NLP. Some of the most commonly used grammar checking tools such as Grammarly gives numerous features that assist individuals in improving their writing. These tools change your average writing into amazing literature.
For instance, if you want to email your employer, and you will write an article or letter, you will certainly use these tools. These tools will correct your spellings, grammar as well as suggesting synonyms that will enhance your vocabulary and readability. This will deliver content with perfect engagement and clarity.
Hiring and Recruitment
The NLP technology has been widely used in hiring and recruiting suitable candidates for various job positions. Candidate searches are done much faster by filtering and sifting through the numerous resumes of applicants who have met the job requirements. With a gender-neutral and bias-proof as well as other relevant keywords for the particular job description, the NLP software for recruitment will maximize the job applicants without leaving any chances of eliminating any potential good candidate during the search process. This technology has been widely used in government recruitment where they need huge numbers of employees. The process will be fast with a high level of accuracy eliminating every chance of bias and discrimination.
Healthcare
With the assistance of health IT systems, in hospitals and other healthcare facilities, doctors are accompanied by minimal subjectivity in providing the ideal medical knowledge as well as decision-making. In this field, NLP comes in hand and helps in numerous ways. With this revolutionary technology, data retrieval for patients is much easier thus speeding up the process of medical intervention. Moreover, doctors and healthcare providers make accurate decisions regarding patients and ensuring the best treatment and diagnosis is achieved. This automatically translates to better living standards among the citizens. In the health sector, this is the trend and it has been well appreciated by the staff since it eliminates the long queues that patients have to make to receive treatment.
Conclusion
Generally, NLP will add value to any business that wants to leverage its unstructured data. This revolutionary technology has been adopted by many organizations and individuals due to its wide applications and the numerous benefits that are associated with it.
The applications initiated by NLP include chatbots, sentiment analysis, Machine translation, query answering, summarization, text extraction, and many more. Although NLP is not yet independent to give human-like experiences, it will solve your business problems and enhance your business as well as customer relations. It will also help your organization to come to a fast decision making thus serving customers most appropriately.