Natural Language Processing (NLP) is an area of Artificial Intelligence that uses computers to interpret human languages, making possible applications such as chatbots, cybersecurity measures, search engines and big data analytics.
Recent advancements have occurred in this field, such as word embedding, neural networks and transfer learning.
Deep learning is a form of machine learning which involves training large neural networks on large amounts of data, providing a powerful technique for predictive analytics, NLP, computer vision and object recognition.
Deep Learning technology has become an indispensable component of modern automation, from driverless cars and voice control in consumer devices such as smartphones to recognition of objects like stop signs, traffic lights and pedestrians.
Deep learning presents several difficulties for its practitioners. Primarily, its training requires large amounts of data – in fact, Facebook recently used one billion images to achieve record image recognition results.
Transfer Learning is a machine learning technique that allows data science teams to take advantage of knowledge from one model that has already been trained on another task that may be closely related. By taking this shortcut approach to building new models, time and data requirements are reduced significantly.
This technique represents a substantial advancement in natural language processing, particularly for computer vision and natural language tasks such as sentiment analysis which require high amounts of computational power.
Pretrained models are readily available on popular platforms such as Keras for use in transfer learning, prediction and feature extraction.
Transfer learning can only be effective if the features learned from one task can be applied to another task successfully, since weights learned in an older task may no longer apply and lead to suboptimal results.
Natural Language Understanding
Natural Language Processing, or NLP for short, refers to how machines can read human text without human interference. NLP technologies have numerous applications ranging from machine translation and customer service automation to machine translation itself.
While most NLP models focus on English, businesses may require processing data in other languages as well. Therefore, having a model capable of accurately interpreting data in multiple languages helps improve accessibility and speed up data translation workflows.
Oft times this requires breaking up content into smaller chunks that can be analysed using machine learning (ML) algorithms. Once separated from one another, these chunks can then be examined for their relationships, dependencies, and context.
Natural Language Generation
NLG (Natural Language Generation), is an AI subset that converts raw data into natural-sounding language. Though NLG technology has existed for some time now, its adoption by enterprises to improve operational efficiencies, human productivity and customer engagement has grown considerably over time.
NLG technology is most often employed for text-based tasks, including email auto-completion and social media automessages, but can also be utilized for more complex procedures. Furthermore, conversational AI technologies like chatbots and virtual assistants make use of NLG as part of their technology platform.
NLG provides several advantages over manual content creation methods, including higher output volume and faster speed, yet has its own set of drawbacks compared to pure manual methods: higher output volume and faster speed as well as being unable to generate texts from scratch without reference. NLG may still prove useful for creating personalized reports or web/mobile content at scale – saving thousands of hours in areas such as product/service promotion, customer support or product maintenance.