
Artificial intelligence has taken the world by storm and revolutionized business operations in various industries. Natural language processing in AI is no exception and is set to transform the IT industry with its immense growth potential. Professionals upskilling themselves with the latest industry knowledge and natural language processing tools will have a competitive advantage over their peers while applying for their dream jobs.
NLP is one of the core foundations of the various tools we frequently use, like translation software, chatbots, spam filters, and search engine assistance. Let’s explore in detail what NLP is in artificial intelligence, how you can learn natural language processing, and the benefits of the natural language processing model.
What is Natural Language Processing in AI?
Natural language processing model, abbreviated as NLP, is a field of artificial intelligence and Machine Learning that allows machines to break down and interpret human language. It involves training computers and enabling them to meaningfully understand, analyze, and respond to human language inputs. The inputs can be in various forms, like text or spoken words.
For those professionals keen on diving deep into the nuances, an AI and ML course will provide you with the required knowledge and in-demand skills to scale your AI and ML career to new heights. These courses train you in the latest advancements in the AI space, such as generative AI, explainable AI, ChatGPT, and many more. Natural language processing courses also comprise capstone projects in various industry verticals that help you apply your knowledge and refine your skills to be a job-ready AI professional.
Besides, the average salary of an NLP engineer in India is Rs. 8 lakhs, potentially going up to 20 lakhs. If you work in the United States, the average compensation is over $95000 yearly.
Common NLP Tasks
NLP is a combination of computational linguistics, statistical analysis, machine learning, and Deep learning models. Few NLP tasks allow machines to break down voice or textual data and understand the input to take the appropriate action. The tasks are elaborated below:
1. Speech recognition
Often known as speech-to-text, it converts voice data into textual data. The text inputs are converted into a language understandable by machines using the text vectorization concept and numerous NLP tools. Every application that accepts voice commands has this speech-to-text function.
2. Speech tagging
It is also called grammatical tagging, where the computer algorithm reads a sentence and classifies the parts of speech like nouns, pronouns, verbs, adjectives, etc. It assists in making the association between the input and corresponding output.
3. Identifying appropriate word sense
It involves training the machine to determine the correct meaning of words having multiple definitions in various scenarios and identifying what makes the most sense in the given context. It uses statistical analysis methods to build its knowledge bank with the data gained over time.
4. Entity recognition
It involves classifying the words or phrases into specific entities. For example, “India” is categorized as a place, whereas Tea is classified as an object. NLP uses the concepts of Lemmatization and Stemming to make it easier for computers to understand the association between different words and grammatical parts of a sentence.
5. Co-reference resolution of entities
This part involves identifying whether two or more words refer to the same entity. If it does, the algorithm places them in the same category even for future reference. For example, consider the following sentence:
- “Rahul went to the store. He bought some packed food.”
The co-reference resolution would identify that “he” refers to “Rahul” in the sentence and use it as a reference for further use.
6. Sentiment analysis
Sentiment analysis is one of the most common NLP tasks, where the natural language processing model is trained to classify the text according to the opinion of the context into positive, neutral, negative, sarcasm, or confusion.
7. Natural Language Generation
After following the above tasks, the machine learning algorithm organizes, converts, and structures the information into a readable human language to choose the appropriate response.
Benefits of Natural Language Processing model
Businesses implementing natural language processing models in their operations gain numerous benefits from this emerging technology.
1. Large Scale data collection and analysis
Natural language processing tools help collect large-scale data by converting information streams into machine-readable text. Much of this data is available in unstructured form, which requires a relatively longer time to parse and analyze. However, with NLP technology, such vast amounts of data can be processed in just a few minutes rather than days or weeks of implementing traditional solutions.
2. Provides more accurate analysis
Unlike the human analysis approach, NLP-powered tools are trained to analyze the human language very well and eliminate the chances of error. Once you train your natural language processing model according to your business needs, data analysis becomes more accurate and provides immensely helpful insights.
3. Quick Text Generation
One of the most popular projects in the field of NLP is Open AI’s Chat-GPT3. It is a brilliant example of natural language processing in AI, which takes input and produces narrative text afterward. You can tweak this narrative to suit the tone according to your requirements. It is still emerging but provides a highly credible text in less time, thus assisting human work. Quick text generation is a fast-developing field in natural language processing models with consistent innovation.
Conclusion
Implementing natural language processing tools and models can greatly boost business organizations by streamlining and automating repetitive processes and helping leaders make data-driven decisions. It also provides numerous growth opportunities to professionals building their careers in artificial intelligence. Individuals who learn natural language processing through various learning mediums and implement their knowledge will potentially have a higher demand to work as an NLP engineer in top IT giants.