Just years ago someone came up with the idea of a robot to understand human speech and text. It was a delusion that could only be found in the paragraphs of science fiction books or themes in movies. Very well known as NLP or Natural Language Processing, the concept of a computer understanding human text and speech is now no longer fictitious.
It is not an easy task to achieve. Earlier there is the difficulty of speech of human in a concise manner so that a machine can recognize. Secondly, the problem of words that sound the same, but have diverse meanings such way and weigh, hair and hare, weight and wait, and so on.
How Natural Language Processing works
Processing the word or spoken relies heavily on Big Data analysis, big amounts of structured, unstructured and semi-structured data that can be mined for useful information. Computers can quickly go through the raw data, analyze it, and find pattern and trends. Primarily, NLP relied on basic rules where machines using algorithms were told what phrases and words to look for in text and then taught specific responses when the phrases appeared. It has evolved into deep learning, a flexible, more automatic method in which algorithms are used to teach a machine to identify a speaker's intent from a series of examples.
In the evolution of NLP, algorithms have been historically bad at interpreting. However, now with improvements in deep learning and Artificial Intelligence, algorithms can now successfully interpret.
If you own a Google Home or an Amazon Echo, then you are interacting with artificial intelligence and Natural Language Processing. In addition, it is already being used in all sorts of business applications including manufacturing, digital marketing customer relations, human resources, business analytics, and healthcare.
Natural language processing is the part of the computer science and artificial intelligence, in which it is researched how to interact with humans as well as computers. This computer shows the ability to understand human language. Under this, the person's language is automatically understood by the computer with the help of any software.
Research has been ongoing in this area for the past 50 years. As the computer grew, the study started in depth. For Example: If you open a website, then there is an option for an online assistant who works automatically on any website. It works only on the basis of the natural language processing process.
NLP is used to analyze human language through which machines understand the language of human beings.
Due to the establishment of a conversation situation between man and computer, many things have benefited, such as automatic text summarization, centimeter analysis, subject analysis, relationship extract, the entity's name Identity and so on
Apart from this, the use of text mining feedback, machine translation, also in question-answer session is also in use.
NLP and Artificial Intelligence
According to the modern approach, Natural Language Processing works on the basis of Deep Learning which is based on Artificial Intelligence. Here, various data patterns are used to understand a program. The data collected under this reaction is prepared to identify according to the relevant conventions.
In the first NLP response, a role-based approach was adopted in which the machine learning format was taken. A lot of words and phrases were interpreted by machine-based algorithms. If a user used those words, the machine was trained to give a specific response.
In the Deep Learning Methods of the NLP, the algorithm is trained to identify the user's intentions on the basis of examples. This response has an easy-to-operate and intuitive approach.
NLP in Digital Marketing
Science fiction is fairly accurate if you start listing the technological predictions in sci-fi movies that have actually turned into household stuff today. The common trait in all these tech predictions is an aspiration to make machines more human-like.
With Siri and Alexa talking their way into our lives, we are conversing with machines that have AI capabilities and give a more intelligent answer every time. Artificial Intelligence is helping computers understand one of the most complex communication hurdles for scientists, understanding the human voice and its countless semantics, social context, dialects, and meanings.
Natural Language Processing (NLP) is a branch of AI that studies computers learning and interpreting in our natural language. NLP is seen as a market worth 16.07 billion USD by 2021. But the big question is why are enterprises betting their money on NLP to enhance their digital marketing strategies?
NLP’s traditional applications were in defense and crime investigation. Today, we use it to convert text to speech. Your email is equipped with spam filters capable of understanding the subject headings and filtering them out. But that’s not all.
NLP is a magical looking glass that can let marketers analyze customer content to extract qualitative customer insights. Imagine you are conversing with your friend about a drone you bought and AI accurately predicts your sentiment towards the drone. Just by listening to or reading your comments. NLP is a powerful tool to see customer data in a new light.
NLP and Machine Learning are technologies that have the potential to disrupt the landscape in business intelligence, marketing, e-commerce and enterprise information systems overall.
In order to fully realize the advantages of using NLP, there needs to be an interaction between these systems and other components of the enterprise. For example, a social prospecting solution is useful if it is connected to a CRM database, allowing the company to augment information on existing customers, and prospective customers.
Similarly, NLP should be a key component in digital marketing platforms offering capabilities such as personalized email, recommendation, and mobile apps.
New advances in NLP such as the incorporation of deep learning are increasing both the accuracy and breadth of capabilities of NLP.
We are moving to an era where critical business decisions and marketing will rely increasingly on unstructured data. By leveraging this till now a largely unexploited treasure trove of data, organizations will be better poised to react in real-time and more importantly, be proactive about their strategy.
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