Natural Language Processing in Today’s World
What is NLP?
Natural Language Processing is a complex and interdisciplinary field that enables machines to comprehend natural language, making it possible for humans to communicate with computers in a way that feels less artificial. Several techniques are implemented for the machines to adapt to human language and behavior and these include tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and machine translation. The machines can analyze text and extract meaning from it, to determine the topic, sentiment, or intent.
Evolution of NLP:
NLP has come a long way since its inception in the 1950s. Early work focused on rule-based systems that were programmed with specific rules for language processing. In the 1980s and 1990s, statistical methods became more prevalent and allowed for more adaptable language models.
In recent years, deep learning methods have led to significant advances in NLP, including the development of powerful language models such as BERT and GPT-3.
Use Cases:
NLP has a wide range of applications in various industries, including finance, marketing, and healthcare.
It is being used more in the healthcare industry to predict outcomes of a patient’s health and to optimize and store health records. Some of the real-time usages are given below:
1. Researchers at Stanford University used Natural Language Processing to analyze the records of patients with type 2 diabetes, following which they trained the model to identify people with uncontrolled diabetes. This model was then used to predict the people at high risk for cardiovascular complications and recommended changes in medication or lifestyle as per their personal needs.
2. The American Association of Retired Persons has successfully implemented Natural Language Processing to modify their existing diet plan. Participants were asked to fill out a form about existing diet plans, and sentiment analysis was done on their survey results.
3. Authorities at the FDA are considering using NLP to arrange and review unstructured data into structured data to maintain precise Electronic Health Records (EHS) and take the paperwork burden off healthcare professionals
Conclusion:
Thus NLP has the potential to revolutionize the way we interact with machines and analyze data. As the field continues to evolve, we will be witnessing its ability to extract insights from unstructured data such as text, audio, and video, to greatly improve productivity and decision-making in a wide range of industries.
