Natural Language Processing is used so commonly today that we take it for granted. We use it with Amazon’s Alexa, Google Home and Translate, voice-to-text dictation on our phones etc. It is practically everywhere and makes our interactions with devices faster, convenient, and easier.
NLP is a branch of Artificial Intelligence (AI) that uses Machine Learning (ML) to understand a text or a voice command’s meaning. “For instance, people ask questions in different ways (word choice, tone of voice, etc.) – One customer might ask,” Can you update me on my last order status?”, Another might ask, “Where is my package?”. In such instances, knowing purpose goes beyond merely understanding the context of individual terms, collecting the appropriate information that suits the customer’s need for its knowledge base, and creating an appropriate response that drives CX.
The global NLP market size will be $27.6 Billion by 2026, from $9.9 Billion in 2020. It is mainly bifurcated into Machine Translation (MT), extraction of data, automated summary, classification of text, analysis of sentiment and others. I firmly believe that, due to the growing demand for data-driven insights to optimise customer experiences, the sentiment analysis category would have the highest growth rate.
NLP – Some Simple Use Cases
Here are some of the most common and widespread NLP applications in different verticals.
NLP allows a wide range of semi-and unstructured textual documents to be used by the medical community, lets patients gain more knowledge of their conditions, make informed decisions by engaging with intelligent chatbots in healthcare. Growing demand for improving EHR, data accessibility, risk mitigation predictive analytics and expanded use of connected devices are fuelling NLP in healthcare.
Deep learning (DL) and NLP technologies are being developed by top tech companies and automakers to enhance the driving experience and effectively cover the distance between a driver and a vehicle. Example – Mercedes-Benz responds to the conversational commands of drivers. Anchored around DL, it often learns, over time, to adapt to their behaviours..
The automotive industry also aims to provide smart home connectivity, the purchase of movie tickets, and the ability to deliver food. Although some of these verticals may require more iterations, with MT, ML and NLP, the possibilities look endless.
Sentiment Analysis on Social Media
Customers are continuously leaving their views on social media sites, a wealth of instant consumer feedback; there is just too much text information to go through manually. That’s why more and more companies are turning to sentiment analysis tools that help you quickly understand how customers feel about your brand by analysing the emotion, language, and urgency of online conversations. Marketers now have what it takes to build actionable plans and make educated choices with sentiment analysis tools.
Google searches or WhatsApp keyboard use NLP to understand the next word you are trying to type. Suppose we type in “what is the weather” in Google search we receive predictions like what is the weather today, what is the weather like in California, what is the weather this weekend etc-based on user’s habits of texting but there is a whole lot of analysing the dataset behind the scene. You can learn more about next word prediction model here.
Smart assistants, such as Amazon’s Alexa, Apple’s Siri and Cortana, are among the first NLP examples. For different questions, many use Siri who understands and provides relevant answers depending on the context being asked. Alexa is commonly used in everyday life to assist people with various things, such as switching on lights, vehicles, geysers, and many other things. Learn more on how a smart speaker works.
Consumers around the world read, watch, or listen to news updates every day and presume that everything they find is real and accurate – many believed and spread a false conspiracy during the US elections of 2016. NLP scientists have been more interested in developing algorithms to detect disinformation over the past few years; here are some AI tools to help you fight yourself against false news. We also have several tech companies working on it already, such as Google using AI to detect altered videos.
1.4 billion people use chatbots everyday and 80% of standard questions can be answered. Tech giants such as Google invent unique criteria to be more human – dialog agent, Meena introduced Sensitivity and Specificity Average (SSA); a way to reduce ‘perplexity’ (helps (emotionally too) to understand and react in the right way). If you want to build one, these are the top open source chatbot platforms.
HR technology is developing at a breakneck speed and so is NLP, which automates a lot of routine and internal human resources activities, communications, training, onboarding, and other work. NLP-based software can set specific work specifications and philtre out CVs that do not fit when importing, extract qualifications, innovations, years of CV experience, and apply this information to each database specialist ‘s profiles. If NLP and AI solutions are properly implemented, they can: provide successful recruiting experiences, minimise administrative burden, increase self-service opportunities for employees and improve onboarding for employees. Here are some more exciting AI applications in HR and top NLP tools to help HR critical functions.
Interactive Voice Response (IVR) is the foundational technology that analyses key words such as upgrading my credit card, making a purchase, and accordingly guides the call to a particular agency. The NLP algorithm working with ML senses and analyses the urgency and routes / prioritises the customer ticket in a similar manner, based on voice modulation and pattern. Check the list of best IVR systems.
NLP – The Technology
The following are some of the key technology-based modules for an efficient NLP solution:
Tokenization – The operation of splitting a string of text into words or phrases is tokenization. To measure word frequency, the resulting list of words / sentences is further analysed. For more information, check this Stanford University resource.
Stopwords – For textual data analysis, stop words tend to be of little use-words that often occur but bear little weight (the, for, is, and, or, was, to, this, that, but, if, in, a, as, etc.) Thus, in some cases, these are omitted from key words in the pre-processing step.
Stemming – Stemming refers to conveying the word’s meaning, with additional affixes such as fast / quickly, eat / eaten / eat, etc. So, we may want to return to the terms stem to better evaluate the vocabulary in a text to minimise datasets and increase relevance.
Lemmatization – As compared to stemming, lemmatization is more efficient and sophisticated and returns more detailed and substantive words / tokens by considering the context in which the term is used in a sentence. It is slower and more complex, though.
Modelling – In order to predict the next item in a text string that relies on the analysis of consecutive objects, different language models are used: letters, phonemes, phrases. In NLP, N-grams are used to evaluate word sequences in particular, so as to measure the frequency and predict the next possible word.
There are many more key terms and tools to build awesome NLP projects, you might want to refer here.
NLP – Futuristic Use Cases
NLP has, without a doubt, acted as an efficient communication bridge between humans and computers, and in terms of accuracy and scalability, NLP models have improved a lot. However, I would still say, NLP is in its toddler stage as there are many advancements in store like,
Automatic Language Translation – Currently, all MT software needs post-editing but can learn to correctly translate prescriptive copies without human help, such as legal contracts or insurance forms. However, it is unlikely that computers can ever reproduce the subtlety and excellence of a professional, human interpreter when it comes to text that needs to engage fully with an audience.
Bidirectional Encoder Representations from Transformers (BERT): BERT is a modern and open-source pre-training model whose bidirectional approach not only enables a model to interpret from left to right, but also to get the full sense of the phrase (similar to understanding the meaning of a phrase). It is difficult to train this unsupervised algorithm, expensive (as of now) but very efficient-can predict, classify, translate, and have enormous potential to transform NLP, let’s wait and watch!
Emotion detection accuracy: NLP is known for its ability to detect emotion and sentiment, but it still needs to develop its ability inside a piece of text. Deep analysis of sentiment is quite complicated, especially with views that still sound positive / negative yet indifferent.
Voice is an untapped medium that can give us and our customers an opportunity for deeper interaction, and we cannot afford to leave that chance on the table. You don’t have to know the technicalities of NLP / ML / DL in detail, services like Amazon Comprehend will help you discover the insights and relationships in your unstructured data. The future of NLP is exciting as developments will be built-in to business revenues with different innovations such as gesture and facial recognition to make them more effective and agile
Before I sign off, here are some interesting facts for you,
Until 2025, 85% of company engagement will be achieved without human contact. Chatbots save companies 8% billion globally. By the end of 2020, 25% of all companies will integrate a chatbot or virtual client assistant using NLP technology into their customer services. The White House and a consortium of leading research organizations have prepared the COVID-19 Open Research Dataset with more than 200,000 academic papers with the current pandemic and this publicly accessible dataset is used with all the recent developments in NLP and other AI techniques to produce new ideas to help the ongoing battle against this infectious disease. Such developments are also expected to drive NLP growth. If you’re interested or want to learn more about how you can use NLP to support your enterprise, please message us, or visit us at www.softclouds.com and schedule a meeting with us today!
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Pratik Jain has over 9+ years of experience in the Information Technology Industry. Pratik is specialized in Knowledge Management (KM) and has architected multiple solutions for international companies. He is highly skilled in developing models and text analytics with extensive experience in optimized intent classification and entity extraction.
Pratik has a deep knowledge of Knowledge Management Principles, Dictionary Construction, Search Configuration and Accuracy tuning and an expert architect on Oracle Knowledge Management. Pratik is a certified professional on Oracle Cloud Infrastructure and Amazon Web Services, and expert on Google Cloud Platform, Microsoft Azure and Elastic Search. Pratik is highly passionate in the integration of technology and business, possesses a highly analytical data-driven personality with a proven record of creating business value.