Natural Language Processing (NLP) is a part of Artificial Intelligence (AI) and cognitive computing concerned with the computer-human language interaction. The study on Natural language Processing began in the 19th century. In 1950, Alan Turing, an English mathematician, computer scientist and cryptanalyst, developed a test known as Imitation game (or Turing Test) to determine if machines are capable of behaving like the humans.
SHRDLU was one of the earliest computer programs. Developed in 1960, it could take human inputs in English and performed some simple actions. Since then, there has been a tremendous advancement in NLP.
What is NLP and How does it work?
A significant part of human communication happens through either by text or voice. And if we look at our daily lives, we can see that many applications and services we use follow the same communication mode.
For a business point of view, almost every business communicates either by talking over the phone or sending messages through email. Imagine a situation where your computer can respond to all these calls and emails just as you would do.
NLP is a technique that provides computers with the ability to understand the human text and spoken words, analyze them and take actions as humans do.
NLP powered with Machine Learning (ML) algorithm and deep learning models, identify and extracts the information, and then convert it into computer language and learn from it.
For instance, if you own a business that handles thousands of calls and emails every day and plans to build NLP chatbots to respond to these calls and emails. The archive of the actual conversation between your chat agents and customers is used as training data for this.
NLP extracts valuable information out of this conversation, analyses them, tries to find a pattern, and uses it in future conversation. It will save a lot of time and effort for any enterprise.
Top Trends In Natural Language Processing in 2021
Businesses worldwide are using social media platforms as a tool to generate a large amount of data. It has become a platform for public opinion as well. If your company targets potential customers in the future, it is essential to understand how your current customers feel about your brand. NLP is the best tool in this regard.
NLP quickly analyses the attitude, emotional state and the language of your customers who are engaging with your brand in the form of social media posts or comments.
This is known as sentiment analysis or opinion mining. Customer opinion provides an easy insight into the performance of your company in the market.
However, the inability to identify the contextual meaning of the words and sentences and spot the usage of sarcasm and ironic statements is still a challenge faced by NLP.
Growth in Multilingual NLP
Majority of techniques and resources in the world are developed for the English language, and the same is the case with NLP. Most NLP advancements are focused only in English till now.
But the scenario seems to change drastically as the technology giants like Google, Facebook and Microsoft are now implementing pre-trained multilingual models that perform much better than the monolingual models.
Microsoft’s Turing Multilingual language model (T-ULRv2) is a significant innovation in the multilingual NLP direction. It is a cross-lingual innovation that incorporates InfoXLM to create a universal model representing 94 languages in the same vector space.
Another example is the M2M-100 model by Facebook. M2M-100 is the first Multilingual Machine Translation model developed by Facebook, capable of translating almost 100 languages and understanding 2,200 language directions without English data interference.
According to Facebook, M2M-100 will allow 2 billion Facebook user to communicate effectively and quickly without relying on English data.
Market knowledge and exchange of information help a business determine the market segmentation, the opportunities, the penetrations and the existing market metrics, in the market it operates or wants to operate in. This is known as Market Intelligence.
Keeping yourself updated with the fast-changing market trends and standards is the key to survive and thrive your business. In this regard, Natural Language Processing empowers a business in tracking and monitoring the Market Intelligence report to extract valuable information and formulate business strategies.
Undeniably, NLP will play a vital role in business organizations’ functioning and planning its future steps.
Customer Service and NLP chatbots
2020 was a challenging year for the customer service as there was a sudden increase in the customer support ticket across industries. Businesses were struggling to deal with this increment and provide an on-time response to the customer queries.
Every company realizes the importance of customer care service. Integration of NLP chatbots will help in tagging and routing customer support ticket. It will save a lot of time and effort, and also improve customer satisfaction.
With increasing customer care demand, it is assumed that NLP chatbots will go through considerable advancements in the future, making it more capable of carrying out a complicated conversation, self-improvement, and learning without previous training.
The success of automation in Machine learning (AutoML) and its ability to deal with the real-world problems led to automation in NLP or AutoNLP. With the help of AutoNLP, it has become easier for businesses to build a model like sentimental analysis with a few basic lines of code and choose the best model for a given data-set. With the continual advancements in NLP, more and more businesses are expected to integrate automation in NLP in future.
From identifying the color of cubes in 1950 to guiding space ships and performing highly complex tasks, the significant growth of NLP doesn’t seem to slow down soon. With pre-trained models, improved accuracy and judging customer emotions, NLP is reshaping enterprises across the globe.
However, it is an undeniable fact that it still struggles to understand basic human language. But the vast advancements in NLP is expected to fill this gap between computers and human language in the near future.