The Future of Natural Language Processing: Trends and Innovations

There are no reasons why today's world is thrilled to see innovations like ChatGPT and GPT/ NLP(Natural Language Processing) deployments, which is known as the defining moment of the history of technology where we can finally create a machine that can mimic human reaction. If someone would have told you that there is a machine a few years back then no one would have believed you. But now here we have chatGPT which can construct anything within everything.

👁 Future-of-NLP-Trends-and-Innovations

What is NLP?

NLP is one of the applications of AI that analyzes a natural language. It refers to the artificial intelligence method of communicating with an intelligent system using natural language. The real-life application of NLP can be seen in our virtual assistants like Siri, google assistant, etc. We all know that these virtual assistants in our mobile are not a human talking to us, it is simply robotic structure, but how a machine can speak and act human-like. This ability has been generated by NLP(natural language processing). It is an ability that taught a machine to respond, read and understand the human language so that the machine can communicate about its inner problem with us. It combines the field of linguistics and computer science to decipher structured language guidelines and to create a machine-learning model that can comprehend and break down significant aspects of sentences.

There are 7 basic steps by which a machine can process natural language, which is as follows

1. Segmentation - It is a process of dividing complex sentences into smaller sentences.
2. Tokenizing - It is the process of breaking simple sentences into words.
3. Stop words - The words that do not play an important role in generating a meaning to the sentence such as and, the, is, etc. are removed from the sentences which are known as stop words.
4. Stemming - It is the process of teaching a basic set of data in sentences which probably means the same. Hence creating awareness in our system.
5. Lemmatization - The addition of emotions and moods to words so that a machine can comprehend the emotional relation of a sentence to a human and hence generate a reasonable response is called lemmatization.
6. Speech Tagging - The process of teaching basic grammar terms such as nouns, verbs, prepositions, etc. to a machine and tagging every word in a sentence as that grammatical term is called speech tagging.
7. Named entity Tagging - To understand the important noun referred to in the document which are popular humans like actors, etc, and tagging them is known as named entity tagging.

With the increase in demand for automated language solutions, today's job market is looking out for professionals in NLP. Hence creating a trend in our job market analysis.

Tools Used For NLP

1. IBM Watson 

IBM Watson NLP  is a tool that has major capabilities which allow you to detect, and extract keywords, emotions, entities, and various other factors.

2. Core NLP 

It is a powerful and quick annotator for discretionary text that is widely used in production. It is largely Java based.

3. Spacy

It is a pre-trained statistical model as well as a word vector. It is a python compatible language. It enables tokenization for more than 49 languages.

4. AllenNLP

It is a sophisticated prototype tool with text-processing features. This tool, when compared to Spacy is not that much used in terms of manufacturing but is used vividly in terms of research analysis.

5. NTLK

It offers a complete set of programs and modules to analyze data in the form of statistical and symbolic analysis using Python. It is one of the most important tools in NLP.

6. GPT- 3 

A very trendy tool created by open AI that helps auto-completing and auto-generating the sentence on its own which is vividly used in the aspect of text prediction.

7. Monkey Learn

It is a simple tool that aids in the extraction of important insights from our data and helps in text analysis such as keyword analysis, etc.

8. Text blob

It is a tool that helps in analyzing segmentation, tokenization, word paraphrasing, translation, speech tagging, etc.

Applications of NLP

Here are some of the basic and daily life applications of NLP, that are used as a normal feature around the world:

• Spell correction (auto-correct updates) - There are times that you might have noticed that when you miss-spell a word while texting, it automatically gets corrected or sometimes you are writing something and the next words are automatically generated. It happens in Word docs or any editor-based app. This is one of the features generated by NLP. 
   
• Search Engines - There are times when you type a word in a search engine and below it appears the list of searches consisting that words by others. The list contains nothing but the trendiest search made by others related to that words. This is also one feature of NLP.
   
• Virtual assistants - Virtual assistant is one of the features that are the most important application of NLP. Every Apple mobile user is familiar with the virtual assistant Siri whom they command to do basic things on their mobile. Every Android user knows about the google assistant that helps you resolve your doubts.
   
• Spam classifiers - When you open your Gmail, you can see all your emails are divided into various fields like promotions, spam, etc. But how does our computer know whether the mail that you receive is spam or not? This work is also done by NLP.
   
• Machine translation - There is various system application that contains a translator like your Google translator which simply translate the sentence said in one language to the required language hence helping us in communicating with people from various parts of the world. This is also a feature generated by NLP.
   
• IBM Watson - It is a type of question-answering system, in which the user asks a question, and the answer to that question is extracted from previous data on their own. It is also an application of NLP.

Future Scope

NLP is a rapidly growing field with a wide range of applications in various sectors of industries. The reason why NLP is trending in various sectors of industries is as the growth of AI-generated machine increases rapidly, there are points when thinking of a human mind is what generate the uniquely possible solution. Using NLP in AI we can communicate with machines and give them our input and create a decision as human-like as possible. This can also make the interaction between machines and humans quite friendly and hence the individual minds can work together to extract an astonishing result. It is predicted that in the future the NLP application is vividly captured in major sectors like health care by organizing medical reports in a way that can be easily found, cyber security by handling the concept of big data, military by increasing the confidentiality of systems, etc.

Conclusion

The future is quite unpredictable with the application of NLP. The more the advancement of the NLP will increase, the more advancement will occur in our day-to-day technology. The power this stream will hold in the future is quite imaginable and hence not only makes us amazed with every aspect but also beholds the wonderment in our life.