Future of Natural Language Processing and Its Paramount Importance

Future of Natural Language Processing and Its Paramount Importance

Hey Readers!! Before going deep let us understand what NLP is?

NLP stands for Natural Language Processing and it is a branch of Artificial Intelligence (AI) which teaches machine to understand, interpret and manipulate human language. With the help of NLP, machine can make sense of written and spoken text. NLP is time and cost efficient as less number of human are needed to be employed to serve the purpose of documentation process and also to identify and extract large database.Though NLP can’t be 100% reliable and training a model is time consuming but NLP fulfil the purpose of various task and it is now considered as one of the fastest growing sector in the field of AI. NLP act as a bridge between human and machine, it uses computational and mathematical methods to analyse human language.

Future Scope Of NLP

With evolution in the field of NLP technology machines will be able to understand humans in a better way and can derive understanding from the unstructured data available online. Even the business sector is reaping the benefits of NLP technology. As the main aspects of businesses is to understand customer intent so many companies are now investing on researchers to build better NLP algorithm which can do sentiment analysis and yield crucial insight from unstructured data, facilitate communication and improve the overall performance. The NLP technology can process language-based data faster than humans.

“A study by Tractia, shows that the market opportunity for NLP is expected to grow to $22.3 billion by the end of 2025.”

So there is huge scope in this field and few probable trends are mentioned below:

  • Virtual Assistants:
    Virtual assistants will become more advance in understanding and responding to complex and real time language conversation. These assistants will be able to converse more like humans, and can perform task like suggesting improvement in business deals, analysing complex request, dictations etc.

  • Extracting Information from unstructured data:
    NLP solutions can retrieve structured data from texts, videos, audios, etc from various source. They will be able to analyse the voice, words choice, positive and negative sentiments of the data to gather analytics. It will also be able to gather data from medical reports, business reports, legal documents, etc.

  • Smart Search:
    NLP systems will use image and object classification methods to help users searching particular object or information. Rather than typing, user will be able to search using voice commands.

  • Transfer Learning:
    Transfer learning is a technique in machine learning (ML) where a model is trained for the main task and then repurposed for another, similar task. Thus instead of developing and training a new model one can just revamp an existing model.

  • The Growth of Multilingual NLP:
    NLP has mainly focused on the English language till date, but now companies like Facebook and Google are partnering with NLP solution providers to introduce pre-trained multilingual NLP models. There is also advancements in multilingual embeddings ,zero-shot learning, language-agnostic sentence which have showed the way to the AI development companies to build multilingual NLP models.

Facebook has recently introduced the first multilingual translation model XLM-R in 2019 and M2M-100 which can translate 100 languages without any data in English.

  • Low-Code Tools took the Hike:
    In 2021, we might see the rise of low code tools in the NLP arena
    which will make things easier. Usually NLP model, requires in-depth knowledge of AI & ML, open-source libraries, coding etc. With low-code or no-code tools more NLP models could be develop.

  • Sentiment Analysis on Social Media:
    From social media plethora of data could be retrieve by the companies and could be a source of data mining. Along with that various online companies could gather feedback, reviews about websites and analyze emotion about how people feel about the products. Sentiment analysis is also used by emails to keep spam out of inbox.

NLP in Healthcare

  • Information retrieval:
    NLP technologies could understand the medical terminology and retrieve relevant medical information which will enable easy access to the right data at the right time.

  • Diagnostic assistance:
    NLP application in healthcare can aid clinicians in diagnosis and symptoms checking. NLP could leverage tools like voice recognition and voice based dictation which could improve patient provider interaction.

  • Image classification and report generation:
    NLP technologies such as automated image captioning could be extended, which will be useful in report generation from images or X-rays. The AI would be able to understand medical images and “post-process” them using deep learning based analytics which will help in predicting certain medical conditions, such as the potential risk of renal failure.

  • Virtual healthcare assistant:
    Intelligent healthcare AI assistant could be created with NLP capabilities which will understand conversations using NLU models enabled with medical vocabulary.These trained model could be able to interpret conversations between a doctor and a patient and summarize the conversation as notes for future reference resulting in less manual labor.

NLP in E-Commerce

  • Customized product recommendations:
    E-Markets could use NLP and machine learning techniques to seek attention and increase customer engagement by analyzing their browsing patterns and shopping trends.

NLP in Real Time Applications

  • Sentiment Analysis:
    It studies the feeling, opinion and classify the expressions as positive, negative and neutral. It is used in social media monitoring, market research, customer feedback analysis, brand monitoring etc.

  • Implementation of chatbots:
    It is a question answering system which stimulate conversation with user through websites, telephone, messaging apps etc.

  • Speech recognition engines used by voice assistant:
    Engines such as Siri, Google assistant, Cortana etc

  • Machine Translation:
    For example Google Translate. It translate word, phrases, web pages between English and other languages.

And many other applications like Spell checking, Keyword searching, Advertisement matching etc.