NLP is a popular technology that is used widely in businesses today. Any feature that relates with languages are incorporating NLP in their operations. According to estimates, the NLP market size is about US$7.5 billion today and it is expected to grow to US$16 billion by 2021. With NLP, machines will be more abled to solve complex business problems. Businesses are turning more towards NLP to derive insights from the huge amount of unstructured data that lies idle in the warehouse.
What is NLP?
NLP combines machine learning, AI and linguistics to allow us to talk to machines as if they were humans. It is also known as computational linguistics. Google’s search engine, Apple’s Siri all use NLP to optimise their results. Previously, effective google search result could be obtained by using the right keywords. After Google introduced semantic search, its algorithm learned associations between words which enables us to ask anything we want instead of specific keyword search. An invisible process translates our queries into a language that the machine understands. This is possible due to NLP. So, NLP gives machines the ability to derive insights from human languages.
The following are some of the applications of the technology that can be used by businesses.
Applications in Customer Service
NLP can be applied in various domains of customer service. Customer service interaction is one of the most insightful areas of businesses as it can throw light on consumer preferences and perceptions. NLP can be used effectively to keep the customers content and happy. Call recordings from customer interactions can be analysed to get more business insights. Chatbot and automated online assistants can provide quick response to immediate simple needs and thus decrease the pressure on customer service representatives. Using speech recognition, NLP can convert spoken language into text. This is used widely in commercial systems with advanced deep learning models. Apple’s Siri and Skype’s translator are examples. NLP can be used to answer questions posed by humans in natural language. This is especially important as mostly natural languages are very crude and unstructured. So, machines deciphering natural languages will be a huge leap forward.
NLP can help in intelligent advertisement targeting and placement. Media buying is usually the largest channel in an organisation’s advertising budget. So, it is important to ensure that the advertisement reaches the right eyeballs. Browsing behaviours, social media and emails contains a lot of information imbedded that can give a lot of insights about consumer preferences. NLP can be used here to match keywords of interest in the texts to target the right consumers. It can also be used for disambiguation or identification of the sense in which a word is used in a sentence.
With increased competition in diverse market, monitoring reputation is essential to avoid getting drifted away in the tide. With a plethora of information sources abut companies like social media, blog posts, and reports, it becomes imperative to utilise these sources to get more insights about the reputation and reviews of the company. According to a survey, about 92 percent of customers read online reviews and 86 percent won’t buy a product with fewer than 3 out of 5 stars. So, with social media platforms revolutionising businesses, it becomes essential to monitor brand reputation.
NLP can help here by doing sentiment analysis that determines attitude or judgement of the writer. It combines NLP and statistics to evaluate the sentiment of the text. It then assigns a polarity to the text in an attempt to figure out the underlying mood. Sentiment analysis is also known as opinion mining. It allows businesses to get a broader public opinion. It can also help in coreference resolution that connects pronouns to the right objects to interpret the text correctly. NLP is the best way to understand and extract insights from these sources.
Use of Neural Machine Translation (MT)
MT is one of the oldest applications of NLP. Neural MT is a new development where MT uses a neural network. Microsoft’s Bing became the first to launch the technology in 2016. MT becomes more efficient over time as NLP feeds more words into the system. Previously MT worked in a unidirectional way, but with neural MT, cross application of data is possible. This radically speeds up the development and thus enables businesses to safely use MT to translate low-impact contents like product reviews, emails, etc.
Helping Hiring Managers
NLP can help hiring managers to filter resumes. Automated candidate sourcing tools can scan CVs of applicants to extract required information and pinpoint the candidates who are right fit for the job. This will save a lot of time and give a more efficient solution.
Market Intelligence and Spams
NLP can help to monitor and track market intelligence. Since markets are influenced by information exchange, using event extraction, NLP can recognise what happens to an entity. Sentence classification can be used to extract relevant information from large repositories. NLP can also be used to identify spams and filter them.
Over the years, organizations have figured out the most promising business applications of NLP to improve their business intelligence. Yet, machines are still not that developed today to process or understand text and voices like humans do. While the future technology is still evolving, NLP can still provide powerful insights for businesses which can help in making efficient decisions.