Natural Language Processing (NLP)

NLP, or Natural Language Processing, is a field in Artificial Intelligence that allows computers to understand and comprehend the languages that are written by Humans. Computer Languages might be complicated for users who are new or aren’t directly involved in this domain. Hence NLP caters to those sets of users, thus saving them the time and resources required to go and learn the language. 

NLP can be further divided into two parts:

• Natural Language Understanding
• Natural Language Generation

Natural Language Understanding

NLU is the branch of AI that further enables the interaction between the computer and the user. This concept allows computers to understand human language without computational syntax. One of the prominent use cases of NLU is the chat and voice bots on various websites that ease user engagement and retention. 

NLU is further divided into two main criteria:

• Intent recognition
• Entity Recognition

Intent recognition is NLU’s way of understanding the user’s sentiment in the text that has been inputted. It is through this process that the computer understands the meaning of the input and adjusts its output to serve the most accurate result.

Entity recognition, on the other hand, is the process of categorizing different groups and entities in a particular message to fit in the right context. Furthermore, the two main types of entities in a message are named entities & numerical entities. Named entities deal with places, names, objects, etc., whereas numerical entities involve phone numbers, age, costs, etc.

Natural Language Generation

NLG takes the computer-generated text to another level but makes it sound humane. More advanced chatbots like ChatGPT do it with the help of contextualizing the topic and then using NLP to produce content that emulates human-written content. Elaborating on the ChatGPT example used earlier, the chatbot by OpenAI is not just creating contextually accurate content, but it also mimics human input to the extent that is yet to be achieved by other search engines in the space. Cases like these in recent times are a testament to the growing demand for NLP from both a business and a consumer point of view.

Importance of NLP

• NLP is evolving sentiment analysis, making computers much more efficient in pinpointing the exact emotional state behind a command. Social media monitoring tools like Hootsuite and Sprout Social use NLP algorithms to analyze customer feedback and comments on social media platforms, providing businesses with insights into consumer sentiment.
• It helps computers use a text-based language, paving the way for scaling numerous language tasks. Google Translate uses NLP to translate text from one language to another, while chatbots use NLP algorithms to understand and respond to user input, thereby enhancing customer service.
• Text-based data sources are highly unstructured. Every human has a different way of communicating, and NLP helps settles this ambiguity and difference. For example, Google's BERT algorithm uses NLP techniques to understand the context and meaning of search queries, providing users with more accurate search results.
• Automatic summarization of text for better problem-solving and solutions is also made possible by NLP. Newsela uses NLP to generate summaries of news articles at different reading levels, making it easier for students and educators to access and understand the news.

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Further Reading

Bias In Machine Learning

A Guide to AI Interpretability Tools

Why Ethics Are Important In Machine Learning

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