‘Data is the new oil’ was a famous quote given by Clive Humby in 2006, and we cannot agree more on this. With the ever-increasing amount of data, there is an acute need for Natural Language Processing (NLP).
NLP technology helps business organizations relate both human and machine forces together and optimally leverage their advantage. The technology that was first studied in the 1950s has now become one of the most dynamic fields of Artificial Intelligence. Moreover, as businesses have adopted chatbots and voice assistants, NLP technology has become even more prominent.
This article will help you understand the nuances of applications of natural language processing (NLP) and why it is crucial for the future.
With NLP, the machines can understand, analyze and gather apt meaning from human conversations. Moreover, it is also known as computational linguistics that allows us to talk to machines. The reason behind it is, NLP is a blend of two various technologies:
- Artificial Intelligence (AI)
- Machine Learning (ML)
Importance of Natural Language Processing
There is a large number of data that is generated through various devices in our digitally-driven world. Be it Facebook, YouTube, WhatsApp, Google searches etc., all these platforms constantly generate a large amount of data in multiple forms.
The problem here is that a large part of the data generated daily is highly unstructured and wayward. As there is no standard approach deployed for the data, NLP comes in as a rescue option.
Better at handling large volumes of data
Big Data technology is the primary medium through which Natural Language Processing has entered the mainstream. NLP is the correct approach that must be applied to cater to large volumes of data.
With NLP technology, the new-age machines are capable of analyzing language-based data in an unbiased and consistent manner.
As we are exposed to a staggering amount of data daily, automation becomes vital if you wish to analyze speech data correctly.
Dealing with a highly unstructured data source
If we say that the text data generated from various social media platforms is a mess, it would be an understatement. The problem with text messages is that they have omitted punctuations, unnecessary use of emojis, abbreviated words, etc., which are tough to decipher. The reason behind this is, human language is highly complex, and it does not have a definitive form.
NLP is highly instrumental in solving the issues that ambiguity in a text message creates. Apart from solving the ambiguity, it adds functional numeric form to the given data.
Top Natural Language Processing Applications to look forward to in 2021
The evolution of Natural Language Processing over the years has been immense. We have listed the top most immaculate natural language processing examples that can considerably impact our lives and would be big this year
Better search engines
You must have seen that even if we type some non-sense words in Google, it predicts the right thing just after the initial letters. We should be thankful to Google natural language processing for this. It enables the search engines to understand the complex human language and carve out the best results for us.
Also, the search autocorrect and auto complete help the user accurately find their desired results. Moreover, it is extensively implemented in the website research tools to formulate the research process and fasten it.
Smarter Grammar checks
It is all because of high-level natural language processing that we have grammar correction tools like Grammarly. The grammar correction tools help in writing grammatically correct content and allow us to send error-free emails.
Smarter Chatbots
There are no second thoughts that integrating AI chatbots in a business play a pivotal role in increasing the customer experience. Moreover, chatbots are efficient, cost-effective, and need no rest, unlike their human counterparts.
All thanks to the NLP technology, modern-day chatbots are even used as online psychologists and personal companions in many cases.
Improved Voice assistants
Apps that can learn languages naturally can be a deciding factor in enhancing the working of voice assistants such as Amazon Alexa or Google Assistant. NL processing helps the voice assistant apps to understand the verbal commands in a better manner. It is why voice assistant apps are very famous among users as they are easy to use.
NLP has allowed users to replace the tedious typing technique with more brilliant voice assistants. Through this, the daily processes are done in a faster way.
Sentiment Analysis
The other word for sentiment analysis is emotion AI. The main focus of Sentiment Analysis is to evaluate the personal records that get recorded in the text. Moreover, it is also a type of data mining used to measure individual thought processes.
For instance, businesses can utilize sentiment analysis to analyze all reviews your business has received about your services or product and assist you in finding if customers are happy, the customers’ pain-points and more details that will gradually help you get better insight to improve your business.
With NLP, the businesses can check whether their customers are happy with their services by implementing top-tier sentiment analysis. The companies will get to know their targeted customers' exact thought processes, which would eventually enhance their services.
Machine Translation
Machine Translation (MT) is automatic software’s use to translate text or speech from one language to another with the help of NL processing. The pitch-perfect example of machine translation is Google Translate, a free multilingual machine translation tool; use to translate text and websites from one language into another using NL processing
- Neural Machine Translation (NMT)
- Rule-based Machine Translation (RMT)
- Hybrid Machine Translation (HMT)
- Statistical Machine Translation (SMT)
Multilanguage NL Processing
Most of the NLPs focus on the English language, but market leaders like Facebook and Google are now looking to roll out more multilingual formats.
For example, the Turing model from Microsoft and the M2M-100 model from Facebook are some path-changing innovations in this field. The M2M model can understand more than 2200 languages and translate 100 languages without using English.
As far as the Turing model from Microsoft is concerned, it includes an information-theoretic framework. This process is used for cross-lingual language model pre-training. Moreover, this model's primary focus is to understand and generate a representation of 94 languages in the same space.
Rise of Deep Learning in NLP
Unlike the past, where NLP problems were based on logistic regression and SVM, recent trends have shown a shift towards Deep Learning for better results.
The primary reason behind the shift is that deep learning techniques can optimize the NLP models. The optimization is based on features such as semantic role labeling, entity recognition, machine translation, dialogue system, contextual embedding, and many more.
Also, researchers believe that there can be two significant innovations for deep learning in NLP in the future.
- Word ambiguity: It is simple as it refers to two words having similar pronunciation but has a different meaning in a different context
- Differentiating between relatedness and similarity
Having authentic market information in an orderly fashion can become a deciding factor in your business's popularity. More so, in today's rapidly changing business environment, NLP can become an essential tool for monitoring the market.
The financial marketing sector is already reaping rich benefits through Natural Language Processing. The technology is used for building better strategies by extracting vital information. Also, it can help in talent acquisition, data visualization, neural machine translation, and many more.
What future has in store for NLP Technology?
To make sense with the ever-increasing amount of data generated every day, you need Natural Language Processing. Moreover, we see extensive use of chatbots in the financial marketing industry to provide a better customer experience.
Digital marketing, on the other hand, is rapidly leaning towards data-driven digital marketing, and NLP has a significant role in that. NLP is no longer just the future. It’s already here.
NLP technology has brought in a paradigm shift in how humans interact with the computer and will continue this in the future. It will be an unprecedented force of transformation in the communication industry in the coming years. Moreover, right from intelligence-driven to data-driven works, Natural language processing will be an essential factor.