Natural Language Processing (NLP) is one of the fastest-growing sectors in the field of artificial intelligence (AI) and machine learning (ML). According to Mordor Intelligence, the global NLP market is projected to reach $42.04 billion by 2026, exhibiting a CAGR of 21.5%.
This exponential growth of NLP has also led to the rise of new trends and innovations in the industry. Let’s discuss some NLP trends to watch out for in 2021.
Transfer Learning
Transfer learning refers to a machine learning (ML) technique in which a model is trained for the main task and then repurposed for another, similar task. So, instead of developing and training a new model from scratch, you can just revamp an existing model.
For example, if you train a classifier to predict whether an image has food items, you can also train it to identify drinks. Similarly, if you train a classifier to recognize clothes, you can also train it to identify shoes.
This helps save time and resources. Since reducing costs will be one of the top priorities for companies moving forward, transfer learning will emerge as a major NLP trend in 2021.
The Growth of Multilingual NLP
While NLP has come a long way, it has mainly focused on the English language to date. This is going to change in 2021 as companies like Facebook and Google are partnering with NLP solution providers to introduce pre-trained multilingual NLP models.
Facebook introduced XLM-R in 2019 and M2M-100 more recently, which is the first multilingual translation model. It can translate 100 languages without any English input or data.
The Rise of Low-Code Tools
Back in the day, if you wanted to develop an NLP model, you required in-depth knowledge of AI & ML, coding, open-source libraries, and much more. Not anymore, as low-code tools are making things easier.
While low-code or no-code tools have been out there for a while, their popularity has been limited to web and software development. In 2021, we might witness the rise of these tools in the NLP arena as well.
MonkeyLearn, a SaaS company, is aiming to democratize ML and NLP, making them accessible to non-technical users. The company has developed a point-and-click model builder that enables you to build, train, and integrate sentimental analysis and text classification models.
The Collaboration of Supervised and Unsupervised Learning
Supervised learning is a machine learning process that includes mapping an input to an output based on example input-output pairs. On the other hand, unsupervised learning doesn’t comprise any input-output pairs or learning models. The model works on its own to discover potential learning possibilities.
Both supervised and unsupervised learning have their set of advantages and drawbacks. While supervised learning is more specific and can be helpful in classification problems, its capability to handle complex tasks is limited. On the other hand, unsupervised learning is beneficial as no labeling data is required, which makes the classification task quicker. But on the downside, it doesn’t allow you to map or estimate results.
To bridge this gap between supervised and unsupervised learning, NLP solution providers are combining the two learning models. This amalgamation of the two models has shown to boost ML models’ performance.
Sentiment Analysis on Social Media
Social media is no longer a place where people would share vacation pics and what they had for lunch. It has become a data mine for companies, as copious amounts of data are generated on social media platforms every day. However, the data is raw, and making sense of all the information manually isn’t possible.
NLP has traditionally been used as a tool to resolve the exact same problem. You can work with a natural language processing company and determine what your customers talk about your brand.
However, the language people use on social media isn’t the same as they use when writing an email or leaving a product review. For example, the word “ridiculous” is a negative word, which means something worthy of ridicule. However, it can casually be used to denote something that is unbelievably or absurdly good. Traditional NLP models may struggle to discriminate between these different meanings of the same word.
To overcome this hurdle, a natural language processing to develop a social media sentiment analysis model. These models are more effective at analyzing emotions and opinions on social media.
NLP in Action
Over the years, NLP solution providers have come up with innovative ways to leverage NLP. Here are some of the best use cases of NLP.
Neural Machine Translation (NML): NML has significantly improved the attempts to imitate professional translation. Microsoft Bing introduced the concept of NML, which is now used by Amazon, Google, and other tech giants.
Creditworthiness Assessment: Banks in developing countries have ditched manual credit assessment procedures. They now use NLP algorithms, which analyze social media activity, geolocation data, browsing behavior, and other insights that help banks make informed credit assessment decisions.
Chatbots: Chatbots have been out there for years, and they’re only getting smarter. Juniper Research predicts that chatbots could handle 75-90% of queries by 2022. Chatbots are one of the best use cases of NLP paired with voice recognition.
Sentiment Analysis: As discussed, sentiment analysis is an essential application of NLP. Companies across the world work with an AI development company to build sentiment analysis models to better understand their customers.
Conclusion
As the applications of NLP continue to grow, it’s safe to predict that NLP will play an influential role in the overall functioning and decision-making of businesses. Organizations will continue to work with AI development companies to explore the benefits of NLP.
Resource: whatech