Foundations Of Natural Language Understanding Training Ppt

Slide 1

This slide gives an overview of NLU, a subsection of Natural Language Processing (NLP) that deals with converting human language into a machine-readable format. Computers can automatically interpret data in seconds thanks to Natural Language Understanding (NLU) and Machine Learning, saving organizations precious hours and money while reviewing troves of client feedback.

Slide 2

This slide states that Natural language Understanding is a branch of natural language processing. NLP and NLU, both, seek to make sense of unstructured data, but there is a distinction between the two.

Instructor Notes:

• NLP studies how computers are trained to understand language and promote "natural" back-and-forth communication between computers and people
• Natural language understanding is concerned with a machine's capacity to comprehend human language. NLU refers to rearranging unstructured data so that machines can "understand" and evaluate it

Slide 3 

This slide lists the use cases for natural language understanding, such as automatic ticket routing, automated reasoning, machine translation and question answering.

Slide 4

This slide states that customer service automation is an excellent corporate example of NLU. Machines can interpret the content of customer support tickets and route them to the appropriate departments without requiring people to open every ticket. This saves customer service employees hundreds of hours and allows them to prioritize urgent requests.

Slide 5

This slide describes that a subject of cognitive science known as automated reasoning is used to mechanically prove mathematical theorems or form logical conclusions regarding a medical diagnosis. It provides machines with a type of thinking or logic, allowing them to infer new facts through deduction.

Instructor Notes: 

Computer algorithms may create conclusions based on previously obtained and processed data. In medicine, for example, using IF-THEN deduction rules, robots may deduce a diagnosis based on past diagnoses.

Slide 6

This slide states that one of the most problematic tasks in NLP and NLU is accurately translating voice or text from one language to another. Machine translation technologies allow you to enter words or upload whole documents and obtain translations in dozens of languages.

Instructor Notes:

Google Translate incorporates optical character recognition (OCR) software, enabling machines to extract text from photos, interpret it, and translate it.

Slide 7

This slide describes that answering questions is a branch of NLP and voice recognition that use NLU to assist computers in understanding natural language inquiries.

Instructor Notes: 

Unless you designate a specific city, virtual assistants will tell you the weather for your present location by default. The purpose of question answering is to respond in the user's native language rather than a list of written replies.

Slide 8

This slide lists the importance of natural language understanding. This is that NLU may be used to assist in the analysis of the unstructured text, analysts believe that NLU and NLP have tremendous development potential as the volume of unstructured text that must be examined is growing.

Instructor Notes:

• NLU may be used to assist in the analysis of the unstructured text: People can express themselves in a variety of ways, and this can differ from person to person. The accurate knowledge of the user is essential for personal assistants to be successful. NLU converts the language's complicated structure into a machine-readable format, allowing for text analysis and for robots to respond to human questions
• Analysts believe that NLU and NLP have tremendous development potential: Computers can undertake language-based analysis in a consistent and unbiased manner 24 hours a day, seven days a week. Given the volume of raw data created every day, NLU and NLP are crucial for effective data analysis. This data can be read, listened to, and analyzed by a well-developed and designed NLU-based application
• The volume of unstructured text that must be examined is growing: Analysts predict a CAGR of more than 20% between 2020 and 2025. According to Markets Insider's 2019 study, the worldwide natural language processing (NLP) industry is anticipated to be valued at $35 billion by 2025. The primary underlying cause for the growth is a shift away from product-centric experiences towards customer-oriented experiences. The growing popularity of smart devices and IoT is also contributing to the general use of NLU

Slide 9

This slide showcases factors that should be considered while selecting natural language understanding solutions, such as language support, result quality, usability, flexibility, and speed.

Instructor Notes:

• Language Support: The language of the input data should be supported by the NLU platform. Currently, the quality of NLU in non-English languages is poorer due to the languages' commercial potential. This is changing, though, as research interest grows
• Result Quality: A successful NLU solution should be able to detect linguistic elements, extract their connections, and apply semantic software to understand the information, regardless of how it is written. Continuous learning, aided by Machine Learning, has the potential to increase the quality of results over time
• Usability: The solution should be simple to use for both technical and non-technical staff. A solution with many interfaces can be explored, allowing a non-tech person (such as a customer care representative) to build this system with input. With the distinct possibility that non-techies may use chatbots, the usability of the program and the convenience of use of the user interface are critical
• Flexibility: It is critical to be adaptable to solution areas. This is accomplished through the NLU solution's training and continuous learning capabilities
• Speed: In conversational AI applications, understanding the language is part of the process, and other components include creating a response or acting in response to the enquiry. As a result, seeing and comprehending the language must be completed fast. However, there may be an exchange between the quality of the findings and the speed at which they are computed. This decision needs to be based upon the application

Slide 10 

This slide states that NLU models are capable of performing flawlessly on a particular and unique task. Other duties, however, might reduce accuracy and precision. It is essential to use objective measurements to compare the performance of systems.

Slide 11 

This slide lists technology giants leading in the natural language understanding ecosystem, such as Google, Microsoft, Amazon, and IBM.