Last updated on December 5th, 2023 at 07:16 am
Natural Language Processing (NLP) is an enthralling field of study in computer science and artificial intelligence, characterised by the complex interaction between computers and human language. The study involves manipulating data with dataframe manipulation, and developing cutting-edge algorithms and models that empower machines to understand, interpret, and generate human language in a way that emulates the intricacies of human communication.
In recent years, NLP has witnessed significant advancements, thanks to the availability of vast amounts of digital data, potent computing resources, and the evolution of machine learning algorithms. As a result, NLP has transformed into an indispensable tool for several industries, including healthcare, finance, marketing, and customer service.
On the other hand, NLG, a captivating branch of artificial intelligence and computational linguistics, is devoted to crafting human-like language from structured data or other input formats. NLG algorithms utilise state-of-the-art techniques to meticulously transform the data using dataframe manipulation, then analyse data, identify patterns, and ingeniously use that information to create text that simulates the natural language format.
How are they changing the world?
NLP finds applications in a myriad of domains, ranging from sentiment analysis to machine translation, and speech recognition.
NLP and NLG have an extensive spectrum of applications, spanning from automated journalism to personalised marketing. In automated journalism, NLG can be employed to produce news articles grounded on structured data, such as sports scores or financial reports. Chatbots and virtual assistants can integrate NLG to engender more conversational and natural responses to user queries. In personalised marketing, NLP and NLG can be utilised to engender custom-tailored product descriptions or marketing messages based on user data.
Let’s delve deeper into each application of NLP and NLG and see how they are impacting the world in the most modern ways.
NLP and NLG for Chatbots and Virtual Assistants
NLP and NLG are trained on a variety of models in order to provide the optimal response for the input(s) provided to them. The use of these models depends on the use-case scenario and the level of complexity for the response/ output. Down below are a few models used for chatbots and virtual assistants:
Machine Learning Based Models
Machine learning (ML) algorithms are statistical approaches that allow computer systems to learn from data and improve performance on a given job over time. Large datasets are utilised to build these models, and a variety of algorithms are employed to detect patterns and links in the data, which are then used to make predictions or choices for chatbots and virtual assistants.
The different types of Machine Learning models used to train chatbots and virtual assistants are:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Transformer Based Models
These models employ a deep learning architecture known as a transformer, which allows them to comprehend the context and provide more human-like replies. Transformers allow the model to capture long-term dependencies and interactions between words and phrases. They are made up of a number of encoding and decoding layers. BERT, GPT, and T5 are the three most often used transformer-based models in chatbots and virtual assistants.
NLP and NLG for automated journalism
NLP and NLG have completely transformed the field of journalism by enabling certain aspects of news production to be automated. NLP is used to gather information from a wide variety of sources such as social media, press releases, and news articles, and then this information is leveraged to generate news stories automatically through the use of NLG techniques.
The primary application of NLP and NLG in automated journalism is the generation of news summaries, which are automatically created by analysing vast quantities of news articles and then identifying the most important information. NLG techniques are then employed to generate a concise summary that captures the core elements of the news story.
Another critical application of NLP and NLG in automated journalism is the generation of data-driven news stories. By using NLP techniques to extract data from a wide range of sources such as government and financial reports, it is possible to create news stories automatically through the use of NLG techniques. These stories can provide insights and analysis that would be challenging for human journalists to produce on their own.
NLP and NLG for personalised marketing
In the realm of personalised marketing, NLP and Natural Language Generation technologies are experiencing a meteoric rise as they enable companies to deliver laser-focused and tailored messages to customers. These technologies have the ability to comb through large volumes of data, encompassing a customer's behaviour and preferences, in order to understand the individual's unique needs and preferences.
By using NLP, companies can meticulously dissect customer interactions with their brand, such as chatbots, emails, and social media interactions, to extract valuable insights into the customer's interests, preferences, and behaviours. This analysis can then be used to craft targeted marketing messages, which are then personalised to each individual customer.
Meanwhile, NLG can be used to generate personalised product recommendations and offers based on a customer's past behaviour and preferences. To illustrate, an online retailer can deploy NLG to create product recommendations for customers based on their previous purchases, browsing history, and other pertinent data.
Conclusion
NLP and NLG have the potential to bring about significant changes to society, ranging from the aforementioned to improved communication across language barriers to enhanced healthcare, finance and other major industries.
If you’re an individual looking to succeed in this enthralling field of data science and learn the various types of machine learning techniques used for NLP and NLG, do consider enrolling into Imarticus Learning's Postgraduate Programme in Data Science & Analytics can help you gain the knowledge and skills you need to succeed in this rapidly growing field.
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