SlideShare a Scribd company logo

Natural Language Processing - Groupings (Associations) Generation

•Download as PPTX, PDF•
•
A
Andrew Ferlitsch

Abstract: This PDSG workshop covers methods to automatically generate word groupings as associations, which can be used to teach associations between objects to pre-school and early school children. Ex. What item does not belong? Cat, Dog, Fire Truck, Bird In this presentation, I will cover how to build categorical and association dictionaries to automatically generate associations of the form, what item does not belong. Level: Intermediate Requirements: One should have some programming knowledge.

Technology
1 of 14
Groupings (Associations) Generation Natural Language Processing Andrew Ferlitsch Quisse.Com
Problem • Develop a method to automate the generation of Associations (Groupings). • Used to teach associations between objects to pre-school and early school children. • Ex. • What item does not belong? Cat, Dog, Fire Truck, Bird
Premise At a young age, we train children initially through rote memorization via trial and error and correction by the adult. When we advance to associations, we are developing their ability to learn from abstraction; that is, how do items relate to each other. In this process, we are use the method of categorical separation. We start with categories that are far apart; thus a child will recognize that a car (category = transportation) is too distant from cat, dog, hamster (category = animal) without yet recognizing the finer relationship of the grouping being household pets. As they succeed at recognizing this coarse level of categorical separation, we narrow the categorical hierarchy to learn how within a category the items are related, to learn less coarse relationships.
Categorical Dictionary - Category { noun: ‘cat’, category: ‘Animal’ }, { noun: ‘dog’, category: ‘Animal’}, { noun: ‘pig’, category: ‘Animal’}, { noun: ‘hamster’, category: ‘Animal’}, { noun: ‘car’, category: ‘Transportation’}, { noun: ‘airplane’, category: ‘Transportation’}, { noun: ‘firetruck’, category: ‘Transportation’} A categorical dictionary consists of three levels. Each entry (e.g., noun) is first differentiated with a top-level category. For example, a noun being either an Animal or Transportation.
Categorical Dictionary - Major { noun: ‘cat’, category: ‘Animal’, major: ‘Mammal’ }, { noun: ‘dog’, category: ‘Animal’, major: ‘Mammal’}, { noun: ‘pig’, category: ‘Animal’, major: ‘Mammal’}, { noun: ‘tiger’, category: ‘Animal’, major: ‘Mammal’}, { noun: ‘hamster’, category: ‘Animal’, major: ‘Mammal’}, { noun: ‘penguin’, category: ‘Animal’, major: ‘Bird’}, { noun: ‘snake’, category: ‘Animal’, major: ‘Reptile’}, { noun: ‘car’, category: ‘Transportation’, major: ‘Land’}, { noun: ‘firetruck’, category: ‘Transportation’, major: ‘Land’}, { noun: ‘airplane’, category: ‘Transportation’, major: ‘Air’}, { noun: ‘boat’, category: ‘Transportation’, major: ‘Water’}, For each entry in the categorical dictionary, a second property (‘major’) was added for the next categorical level to form a two-tier hierarchy. For example, for Animal, we have a major subcategory of mammal, fish, bird, reptile, and amphibian.
Categorical Dictionary - Minor { noun: ‘cat’, category: ‘Animal’, major: ‘Mammal’, minor: ‘Pet’ }, { noun: ‘dog’, category: ‘Animal’, major: ‘Mammal’, minor: ‘Pet’}, { noun: ‘pig’, category: ‘Animal’, major: ‘Mammal’, minor: ‘Farm’}, { noun: ‘tiger’, category: ‘Animal’, major: ‘Mammal’, minor: ‘Wild’}, { noun: ‘hamster’, category: ‘Animal’, major: ‘Mammal’, minor: ‘Pet’}, { noun: ‘penguin’, category: ‘Animal’, major: ‘Bird’, minor: ‘Wild’}, For each entry in the categorical dictionary, a third property (‘minor’) was added for the next categorical level to form a three-tier hierarchy. For example, for Animal->Mammal, we have a minor subcategory of wild, farm and pet.
Categorical Dictionary - Blanks { noun: ‘animal’, category: ‘Animal’, major: ‘’, minor: ‘’ }, { noun: ‘bird’, category: ‘Animal’, major: ‘Bird’, minor: ‘’ }, No Distinction No Distinction For some nouns, like bird, there is no distinction beyond the category or major subcategory level. In this case, the next level is left blank.
Categorical Dictionary – Multiple Entries For other nouns, they can belong to multiple minor subcategories. For example penguins can be both wild and flightless. In this case, the minor subcategory is a list. { noun: ‘penguin’, category: ‘Animal’, major: ‘Bird’, minor: [ ‘Wild’, ‘Flightless’ ] },
Association Dictionary { group: ‘Flightless Birds’, category: ‘Animal’, major: ‘Bird’, minor: ‘Flightless’ }, { group: ‘Birds of Prey’, category: ‘Animal’, major: ‘Bird’, minor: ‘Predator’ }. { group: ‘Farm Animals’, category: ‘Animal’, major: [ ‘Mammal’, ‘Bird’], minor: ‘Farm’ } The construction of the dictionary is similar to that of the categorical dictionary. The group property holds a text string that is the primary key and a description of the association. For each association, the property values for category, major and minor are used to search for entries in the categorical dictionary that would be in the association.
Quiz Generation { group: ‘Flightless Birds’, category: ‘Animal’, major: ‘Bird’, minor: ‘Flightless’ } => { noun: ‘hawk’, category: ‘Animal’, major: ‘Bird’, minor: [ ‘Wild’, ‘Predator’ ] }, { noun: ‘falcon’, category: ‘Animal’, major: ‘Bird’, minor: [ ‘Wild’, ‘Predator’ ] }, … For each association selected (e.g., Birds of Prey), the category, and major/ minor subcategories of the association is applied to the categorical dictionary for a matching list of nouns
Quiz Generation From the matching list, three nouns are selected by random (e.g., Hawk, Condor, Eagle}. A question is then formed of the following format: Which of the following does not belong? <noun1>, <noun2>, <noun3>, <noun4> An answer is then formed of the following format, where description is the description of the association (e.g., Birds of Prey). A <noun-not> does not belong to the group <description>
Level 1 Difficulty At the first level of difficulty, the category of the association (e.g., Animal) is used to search the categorical dictionary for all entries not matching the category. From the resultant matching list, one entry is chosen at random. { group: ‘Flightless Birds’, category: ‘Animal’, major: ‘Bird’, minor: ‘Flightless’ } => { noun: ‘car’, category: ‘Transportation’}, { noun: ‘firetruck’, category: ‘Transportation’} … From the matching list, one noun is randomly selected. The noun is then inserted into the remaining placeholder of the question and <noun-not> placeholder of the answer. Which of the following does not belong? falcon, hawk, firetruck, eagle A firetruck does not belong to the group Birds of Prey.
Level 2 Difficulty At the second level of difficulty, the category and the major subcategory of the association (e.g., Animal->Bird) are used to search the categorical dictionary for all entries that match the category, but do not match the major. { group: ‘Flightless Birds’, category: ‘Animal’, major: ‘Bird’, minor: ‘Flightless’ } => { noun: ‘tiger’, category: ‘Animal’, major: ‘Mammal’}, { noun: ‘pig’, category: ‘Animal’, major: ‘Mammal’} … From the matching list, one noun is randomly selected (e.g., tiger). Which of the following does not belong? falcon, tiger, hawk, eagle A tiger does not belong to the group Birds of Prey.
Level 3 Difficulty At the third level of difficulty, the category and the major/minor subcategories of the association (e.g., Animal->Bird->Predator) are used to search the categorical dictionary for all entries that match the category and major subcategory, but do not match the minor subcategory. { group: ‘Flightless Birds’, category: ‘Animal’, major: ‘Bird’, minor: ‘Flightless’ } => { noun: ‘parrot’, category: ‘Animal’, major: ‘Bird’, minor: ‘Pet’}, { noun: ‘cockatoo’, category: ‘Animal’, major: ‘Bird’, minor: ‘Pet’} … From the matching list, one noun is randomly selected (e.g., tiger). Which of the following does not belong? falcon, eagle, cockatoo, hawk A cockatoo does not belong to the group Birds of Prey.

Recommended

AI - Intelligent Agents
AI - Intelligent AgentsAI - Intelligent Agents
AI - Intelligent AgentsAndrew Ferlitsch
 
Pareto Principle Applied to QA
Pareto Principle Applied to QAPareto Principle Applied to QA
Pareto Principle Applied to QAAndrew Ferlitsch
 
Whiteboarding Coding Challenges in Python
Whiteboarding Coding Challenges in PythonWhiteboarding Coding Challenges in Python
Whiteboarding Coding Challenges in PythonAndrew Ferlitsch
 
Object Oriented Programming Principles
Object Oriented Programming PrinciplesObject Oriented Programming Principles
Object Oriented Programming PrinciplesAndrew Ferlitsch
 
Python - OOP Programming
Python - OOP ProgrammingPython - OOP Programming
Python - OOP ProgrammingAndrew Ferlitsch
 
Python - Installing and Using Python and Jupyter Notepad
Python - Installing and Using Python and Jupyter NotepadPython - Installing and Using Python and Jupyter Notepad
Python - Installing and Using Python and Jupyter NotepadAndrew Ferlitsch
 
Natural Language Provessing - Handling Narrarive Fields in Datasets for Class...
Natural Language Provessing - Handling Narrarive Fields in Datasets for Class...Natural Language Provessing - Handling Narrarive Fields in Datasets for Class...
Natural Language Provessing - Handling Narrarive Fields in Datasets for Class...Andrew Ferlitsch
 
Machine Learning - Introduction to Recurrent Neural Networks
Machine Learning - Introduction to Recurrent Neural NetworksMachine Learning - Introduction to Recurrent Neural Networks
Machine Learning - Introduction to Recurrent Neural NetworksAndrew Ferlitsch
 

Recommended

AI - Intelligent Agents
AI - Intelligent AgentsAI - Intelligent Agents
AI - Intelligent AgentsAndrew Ferlitsch
 
Pareto Principle Applied to QA
Pareto Principle Applied to QAPareto Principle Applied to QA
Pareto Principle Applied to QAAndrew Ferlitsch
 
Whiteboarding Coding Challenges in Python
Whiteboarding Coding Challenges in PythonWhiteboarding Coding Challenges in Python
Whiteboarding Coding Challenges in PythonAndrew Ferlitsch
 
Object Oriented Programming Principles
Object Oriented Programming PrinciplesObject Oriented Programming Principles
Object Oriented Programming PrinciplesAndrew Ferlitsch
 
Python - OOP Programming
Python - OOP ProgrammingPython - OOP Programming
Python - OOP ProgrammingAndrew Ferlitsch
 
Python - Installing and Using Python and Jupyter Notepad
Python - Installing and Using Python and Jupyter NotepadPython - Installing and Using Python and Jupyter Notepad
Python - Installing and Using Python and Jupyter NotepadAndrew Ferlitsch
 
Natural Language Provessing - Handling Narrarive Fields in Datasets for Class...
Natural Language Provessing - Handling Narrarive Fields in Datasets for Class...Natural Language Provessing - Handling Narrarive Fields in Datasets for Class...
Natural Language Provessing - Handling Narrarive Fields in Datasets for Class...Andrew Ferlitsch
 
Machine Learning - Introduction to Recurrent Neural Networks
Machine Learning - Introduction to Recurrent Neural NetworksMachine Learning - Introduction to Recurrent Neural Networks
Machine Learning - Introduction to Recurrent Neural NetworksAndrew Ferlitsch
 
Machine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural NetworksMachine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural NetworksAndrew Ferlitsch
 
Machine Learning - Introduction to Neural Networks
Machine Learning - Introduction to Neural NetworksMachine Learning - Introduction to Neural Networks
Machine Learning - Introduction to Neural NetworksAndrew Ferlitsch
 
Python - Numpy/Pandas/Matplot Machine Learning Libraries
Python - Numpy/Pandas/Matplot Machine Learning LibrariesPython - Numpy/Pandas/Matplot Machine Learning Libraries
Python - Numpy/Pandas/Matplot Machine Learning LibrariesAndrew Ferlitsch
 
Machine Learning - Accuracy and Confusion Matrix
Machine Learning - Accuracy and Confusion MatrixMachine Learning - Accuracy and Confusion Matrix
Machine Learning - Accuracy and Confusion MatrixAndrew Ferlitsch
 
Machine Learning - Ensemble Methods
Machine Learning - Ensemble MethodsMachine Learning - Ensemble Methods
Machine Learning - Ensemble MethodsAndrew Ferlitsch
 
ML - Multiple Linear Regression
ML - Multiple Linear RegressionML - Multiple Linear Regression
ML - Multiple Linear RegressionAndrew Ferlitsch
 
ML - Simple Linear Regression
ML - Simple Linear RegressionML - Simple Linear Regression
ML - Simple Linear RegressionAndrew Ferlitsch
 
Machine Learning - Dummy Variable Conversion
Machine Learning - Dummy Variable ConversionMachine Learning - Dummy Variable Conversion
Machine Learning - Dummy Variable ConversionAndrew Ferlitsch
 
Machine Learning - Splitting Datasets
Machine Learning - Splitting DatasetsMachine Learning - Splitting Datasets
Machine Learning - Splitting DatasetsAndrew Ferlitsch
 
Machine Learning - Dataset Preparation
Machine Learning - Dataset PreparationMachine Learning - Dataset Preparation
Machine Learning - Dataset PreparationAndrew Ferlitsch
 
Machine Learning - Introduction to Tensorflow
Machine Learning - Introduction to TensorflowMachine Learning - Introduction to Tensorflow
Machine Learning - Introduction to TensorflowAndrew Ferlitsch
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine LearningAndrew Ferlitsch
 
AI - Introduction to Dynamic Programming
AI - Introduction to Dynamic ProgrammingAI - Introduction to Dynamic Programming
AI - Introduction to Dynamic ProgrammingAndrew Ferlitsch
 
Statistics - SoftMax Equation
Statistics - SoftMax EquationStatistics - SoftMax Equation
Statistics - SoftMax EquationAndrew Ferlitsch
 
Statistics - ArgMax Equation
Statistics - ArgMax EquationStatistics - ArgMax Equation
Statistics - ArgMax EquationAndrew Ferlitsch
 
Statistics - Basics
Statistics - BasicsStatistics - Basics
Statistics - BasicsAndrew Ferlitsch
 
AI - Introduction to Reinforcement Learning
AI - Introduction to Reinforcement LearningAI - Introduction to Reinforcement Learning
AI - Introduction to Reinforcement LearningAndrew Ferlitsch
 
Machine Learning - Neural Networks - Perceptron
Machine Learning - Neural Networks - PerceptronMachine Learning - Neural Networks - Perceptron
Machine Learning - Neural Networks - PerceptronAndrew Ferlitsch
 
AI - Introduction to Bellman Equations
AI - Introduction to Bellman EquationsAI - Introduction to Bellman Equations
AI - Introduction to Bellman EquationsAndrew Ferlitsch
 
AI - Introduction to Markov Principles
AI - Introduction to Markov PrinciplesAI - Introduction to Markov Principles
AI - Introduction to Markov PrinciplesAndrew Ferlitsch
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxnull - The Open Security Community
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 

More Related Content

More from Andrew Ferlitsch

Machine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural NetworksMachine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural NetworksAndrew Ferlitsch
 
Machine Learning - Introduction to Neural Networks
Machine Learning - Introduction to Neural NetworksMachine Learning - Introduction to Neural Networks
Machine Learning - Introduction to Neural NetworksAndrew Ferlitsch
 
Python - Numpy/Pandas/Matplot Machine Learning Libraries
Python - Numpy/Pandas/Matplot Machine Learning LibrariesPython - Numpy/Pandas/Matplot Machine Learning Libraries
Python - Numpy/Pandas/Matplot Machine Learning LibrariesAndrew Ferlitsch
 
Machine Learning - Accuracy and Confusion Matrix
Machine Learning - Accuracy and Confusion MatrixMachine Learning - Accuracy and Confusion Matrix
Machine Learning - Accuracy and Confusion MatrixAndrew Ferlitsch
 
Machine Learning - Ensemble Methods
Machine Learning - Ensemble MethodsMachine Learning - Ensemble Methods
Machine Learning - Ensemble MethodsAndrew Ferlitsch
 
ML - Multiple Linear Regression
ML - Multiple Linear RegressionML - Multiple Linear Regression
ML - Multiple Linear RegressionAndrew Ferlitsch
 
ML - Simple Linear Regression
ML - Simple Linear RegressionML - Simple Linear Regression
ML - Simple Linear RegressionAndrew Ferlitsch
 
Machine Learning - Dummy Variable Conversion
Machine Learning - Dummy Variable ConversionMachine Learning - Dummy Variable Conversion
Machine Learning - Dummy Variable ConversionAndrew Ferlitsch
 
Machine Learning - Splitting Datasets
Machine Learning - Splitting DatasetsMachine Learning - Splitting Datasets
Machine Learning - Splitting DatasetsAndrew Ferlitsch
 
Machine Learning - Dataset Preparation
Machine Learning - Dataset PreparationMachine Learning - Dataset Preparation
Machine Learning - Dataset PreparationAndrew Ferlitsch
 
Machine Learning - Introduction to Tensorflow
Machine Learning - Introduction to TensorflowMachine Learning - Introduction to Tensorflow
Machine Learning - Introduction to TensorflowAndrew Ferlitsch
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine LearningAndrew Ferlitsch
 
AI - Introduction to Dynamic Programming
AI - Introduction to Dynamic ProgrammingAI - Introduction to Dynamic Programming
AI - Introduction to Dynamic ProgrammingAndrew Ferlitsch
 
Statistics - SoftMax Equation
Statistics - SoftMax EquationStatistics - SoftMax Equation
Statistics - SoftMax EquationAndrew Ferlitsch
 
Statistics - ArgMax Equation
Statistics - ArgMax EquationStatistics - ArgMax Equation
Statistics - ArgMax EquationAndrew Ferlitsch
 
AI - Introduction to Reinforcement Learning
AI - Introduction to Reinforcement LearningAI - Introduction to Reinforcement Learning
AI - Introduction to Reinforcement LearningAndrew Ferlitsch
 
Machine Learning - Neural Networks - Perceptron
Machine Learning - Neural Networks - PerceptronMachine Learning - Neural Networks - Perceptron
Machine Learning - Neural Networks - PerceptronAndrew Ferlitsch
 
AI - Introduction to Bellman Equations
AI - Introduction to Bellman EquationsAI - Introduction to Bellman Equations
AI - Introduction to Bellman EquationsAndrew Ferlitsch
 
AI - Introduction to Markov Principles
AI - Introduction to Markov PrinciplesAI - Introduction to Markov Principles
AI - Introduction to Markov PrinciplesAndrew Ferlitsch
 

More from Andrew Ferlitsch (20)

Machine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural NetworksMachine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural Networks
 
Machine Learning - Introduction to Neural Networks
Machine Learning - Introduction to Neural NetworksMachine Learning - Introduction to Neural Networks
Machine Learning - Introduction to Neural Networks
 
Python - Numpy/Pandas/Matplot Machine Learning Libraries
Python - Numpy/Pandas/Matplot Machine Learning LibrariesPython - Numpy/Pandas/Matplot Machine Learning Libraries
Python - Numpy/Pandas/Matplot Machine Learning Libraries
 
Machine Learning - Accuracy and Confusion Matrix
Machine Learning - Accuracy and Confusion MatrixMachine Learning - Accuracy and Confusion Matrix
Machine Learning - Accuracy and Confusion Matrix
 
Machine Learning - Ensemble Methods
Machine Learning - Ensemble MethodsMachine Learning - Ensemble Methods
Machine Learning - Ensemble Methods
 
ML - Multiple Linear Regression
ML - Multiple Linear RegressionML - Multiple Linear Regression
ML - Multiple Linear Regression
 
ML - Simple Linear Regression
ML - Simple Linear RegressionML - Simple Linear Regression
ML - Simple Linear Regression
 
Machine Learning - Dummy Variable Conversion
Machine Learning - Dummy Variable ConversionMachine Learning - Dummy Variable Conversion
Machine Learning - Dummy Variable Conversion
 
Machine Learning - Splitting Datasets
Machine Learning - Splitting DatasetsMachine Learning - Splitting Datasets
Machine Learning - Splitting Datasets
 
Machine Learning - Dataset Preparation
Machine Learning - Dataset PreparationMachine Learning - Dataset Preparation
Machine Learning - Dataset Preparation
 
Machine Learning - Introduction to Tensorflow
Machine Learning - Introduction to TensorflowMachine Learning - Introduction to Tensorflow
Machine Learning - Introduction to Tensorflow
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine Learning
 
AI - Introduction to Dynamic Programming
AI - Introduction to Dynamic ProgrammingAI - Introduction to Dynamic Programming
AI - Introduction to Dynamic Programming
 
Statistics - SoftMax Equation
Statistics - SoftMax EquationStatistics - SoftMax Equation
Statistics - SoftMax Equation
 
Statistics - ArgMax Equation
Statistics - ArgMax EquationStatistics - ArgMax Equation
Statistics - ArgMax Equation
 
Statistics - Basics
Statistics - BasicsStatistics - Basics
Statistics - Basics
 
AI - Introduction to Reinforcement Learning
AI - Introduction to Reinforcement LearningAI - Introduction to Reinforcement Learning
AI - Introduction to Reinforcement Learning
 
Machine Learning - Neural Networks - Perceptron
Machine Learning - Neural Networks - PerceptronMachine Learning - Neural Networks - Perceptron
Machine Learning - Neural Networks - Perceptron
 
AI - Introduction to Bellman Equations
AI - Introduction to Bellman EquationsAI - Introduction to Bellman Equations
AI - Introduction to Bellman Equations
 
AI - Introduction to Markov Principles
AI - Introduction to Markov PrinciplesAI - Introduction to Markov Principles
AI - Introduction to Markov Principles
 

Recently uploaded

CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024Neo4j
 
costume and set research powerpoint presentation
costume and set research powerpoint presentationcostume and set research powerpoint presentation
costume and set research powerpoint presentationphoebematthew05
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationRidwan Fadjar
 
APIForce Zurich 5 April Automation LPDG
APIForce Zurich 5 April Automation LPDGAPIForce Zurich 5 April Automation LPDG
APIForce Zurich 5 April Automation LPDGMarianaLemus7
 
Bluetooth Controlled Car with Arduino.pdf
Bluetooth Controlled Car with Arduino.pdfBluetooth Controlled Car with Arduino.pdf
Bluetooth Controlled Car with Arduino.pdfngoud9212
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitecturePixlogix Infotech
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 3652toLead Limited
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationSafe Software
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...shyamraj55
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024Scott Keck-Warren
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsMiki Katsuragi
 

Recently uploaded (20)

E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024
 
costume and set research powerpoint presentation
costume and set research powerpoint presentationcostume and set research powerpoint presentation
costume and set research powerpoint presentation
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 Presentation
 
Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service
Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort ServiceHot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service
Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service
 
APIForce Zurich 5 April Automation LPDG
APIForce Zurich 5 April Automation LPDGAPIForce Zurich 5 April Automation LPDG
APIForce Zurich 5 April Automation LPDG
 
Vulnerability_Management_GRC_by Sohang Sengupta.pptx
Vulnerability_Management_GRC_by Sohang Sengupta.pptxVulnerability_Management_GRC_by Sohang Sengupta.pptx
Vulnerability_Management_GRC_by Sohang Sengupta.pptx
 
Bluetooth Controlled Car with Arduino.pdf
Bluetooth Controlled Car with Arduino.pdfBluetooth Controlled Car with Arduino.pdf
Bluetooth Controlled Car with Arduino.pdf
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering Tips
 

Natural Language Processing - Groupings (Associations) Generation

  • 1. Groupings (Associations) Generation Natural Language Processing Andrew Ferlitsch Quisse.Com
  • 2. Problem • Develop a method to automate the generation of Associations (Groupings). • Used to teach associations between objects to pre-school and early school children. • Ex. • What item does not belong? Cat, Dog, Fire Truck, Bird
  • 3. Premise At a young age, we train children initially through rote memorization via trial and error and correction by the adult. When we advance to associations, we are developing their ability to learn from abstraction; that is, how do items relate to each other. In this process, we are use the method of categorical separation. We start with categories that are far apart; thus a child will recognize that a car (category = transportation) is too distant from cat, dog, hamster (category = animal) without yet recognizing the finer relationship of the grouping being household pets. As they succeed at recognizing this coarse level of categorical separation, we narrow the categorical hierarchy to learn how within a category the items are related, to learn less coarse relationships.
  • 4. Categorical Dictionary - Category { noun: ‘cat’, category: ‘Animal’ }, { noun: ‘dog’, category: ‘Animal’}, { noun: ‘pig’, category: ‘Animal’}, { noun: ‘hamster’, category: ‘Animal’}, { noun: ‘car’, category: ‘Transportation’}, { noun: ‘airplane’, category: ‘Transportation’}, { noun: ‘firetruck’, category: ‘Transportation’} A categorical dictionary consists of three levels. Each entry (e.g., noun) is first differentiated with a top-level category. For example, a noun being either an Animal or Transportation.
  • 5. Categorical Dictionary - Major { noun: ‘cat’, category: ‘Animal’, major: ‘Mammal’ }, { noun: ‘dog’, category: ‘Animal’, major: ‘Mammal’}, { noun: ‘pig’, category: ‘Animal’, major: ‘Mammal’}, { noun: ‘tiger’, category: ‘Animal’, major: ‘Mammal’}, { noun: ‘hamster’, category: ‘Animal’, major: ‘Mammal’}, { noun: ‘penguin’, category: ‘Animal’, major: ‘Bird’}, { noun: ‘snake’, category: ‘Animal’, major: ‘Reptile’}, { noun: ‘car’, category: ‘Transportation’, major: ‘Land’}, { noun: ‘firetruck’, category: ‘Transportation’, major: ‘Land’}, { noun: ‘airplane’, category: ‘Transportation’, major: ‘Air’}, { noun: ‘boat’, category: ‘Transportation’, major: ‘Water’}, For each entry in the categorical dictionary, a second property (‘major’) was added for the next categorical level to form a two-tier hierarchy. For example, for Animal, we have a major subcategory of mammal, fish, bird, reptile, and amphibian.
  • 6. Categorical Dictionary - Minor { noun: ‘cat’, category: ‘Animal’, major: ‘Mammal’, minor: ‘Pet’ }, { noun: ‘dog’, category: ‘Animal’, major: ‘Mammal’, minor: ‘Pet’}, { noun: ‘pig’, category: ‘Animal’, major: ‘Mammal’, minor: ‘Farm’}, { noun: ‘tiger’, category: ‘Animal’, major: ‘Mammal’, minor: ‘Wild’}, { noun: ‘hamster’, category: ‘Animal’, major: ‘Mammal’, minor: ‘Pet’}, { noun: ‘penguin’, category: ‘Animal’, major: ‘Bird’, minor: ‘Wild’}, For each entry in the categorical dictionary, a third property (‘minor’) was added for the next categorical level to form a three-tier hierarchy. For example, for Animal->Mammal, we have a minor subcategory of wild, farm and pet.
  • 7. Categorical Dictionary - Blanks { noun: ‘animal’, category: ‘Animal’, major: ‘’, minor: ‘’ }, { noun: ‘bird’, category: ‘Animal’, major: ‘Bird’, minor: ‘’ }, No Distinction No Distinction For some nouns, like bird, there is no distinction beyond the category or major subcategory level. In this case, the next level is left blank.
  • 8. Categorical Dictionary – Multiple Entries For other nouns, they can belong to multiple minor subcategories. For example penguins can be both wild and flightless. In this case, the minor subcategory is a list. { noun: ‘penguin’, category: ‘Animal’, major: ‘Bird’, minor: [ ‘Wild’, ‘Flightless’ ] },
  • 9. Association Dictionary { group: ‘Flightless Birds’, category: ‘Animal’, major: ‘Bird’, minor: ‘Flightless’ }, { group: ‘Birds of Prey’, category: ‘Animal’, major: ‘Bird’, minor: ‘Predator’ }. { group: ‘Farm Animals’, category: ‘Animal’, major: [ ‘Mammal’, ‘Bird’], minor: ‘Farm’ } The construction of the dictionary is similar to that of the categorical dictionary. The group property holds a text string that is the primary key and a description of the association. For each association, the property values for category, major and minor are used to search for entries in the categorical dictionary that would be in the association.
  • 10. Quiz Generation { group: ‘Flightless Birds’, category: ‘Animal’, major: ‘Bird’, minor: ‘Flightless’ } => { noun: ‘hawk’, category: ‘Animal’, major: ‘Bird’, minor: [ ‘Wild’, ‘Predator’ ] }, { noun: ‘falcon’, category: ‘Animal’, major: ‘Bird’, minor: [ ‘Wild’, ‘Predator’ ] }, … For each association selected (e.g., Birds of Prey), the category, and major/ minor subcategories of the association is applied to the categorical dictionary for a matching list of nouns
  • 11. Quiz Generation From the matching list, three nouns are selected by random (e.g., Hawk, Condor, Eagle}. A question is then formed of the following format: Which of the following does not belong? <noun1>, <noun2>, <noun3>, <noun4> An answer is then formed of the following format, where description is the description of the association (e.g., Birds of Prey). A <noun-not> does not belong to the group <description>
  • 12. Level 1 Difficulty At the first level of difficulty, the category of the association (e.g., Animal) is used to search the categorical dictionary for all entries not matching the category. From the resultant matching list, one entry is chosen at random. { group: ‘Flightless Birds’, category: ‘Animal’, major: ‘Bird’, minor: ‘Flightless’ } => { noun: ‘car’, category: ‘Transportation’}, { noun: ‘firetruck’, category: ‘Transportation’} … From the matching list, one noun is randomly selected. The noun is then inserted into the remaining placeholder of the question and <noun-not> placeholder of the answer. Which of the following does not belong? falcon, hawk, firetruck, eagle A firetruck does not belong to the group Birds of Prey.
  • 13. Level 2 Difficulty At the second level of difficulty, the category and the major subcategory of the association (e.g., Animal->Bird) are used to search the categorical dictionary for all entries that match the category, but do not match the major. { group: ‘Flightless Birds’, category: ‘Animal’, major: ‘Bird’, minor: ‘Flightless’ } => { noun: ‘tiger’, category: ‘Animal’, major: ‘Mammal’}, { noun: ‘pig’, category: ‘Animal’, major: ‘Mammal’} … From the matching list, one noun is randomly selected (e.g., tiger). Which of the following does not belong? falcon, tiger, hawk, eagle A tiger does not belong to the group Birds of Prey.
  • 14. Level 3 Difficulty At the third level of difficulty, the category and the major/minor subcategories of the association (e.g., Animal->Bird->Predator) are used to search the categorical dictionary for all entries that match the category and major subcategory, but do not match the minor subcategory. { group: ‘Flightless Birds’, category: ‘Animal’, major: ‘Bird’, minor: ‘Flightless’ } => { noun: ‘parrot’, category: ‘Animal’, major: ‘Bird’, minor: ‘Pet’}, { noun: ‘cockatoo’, category: ‘Animal’, major: ‘Bird’, minor: ‘Pet’} … From the matching list, one noun is randomly selected (e.g., tiger). Which of the following does not belong? falcon, eagle, cockatoo, hawk A cockatoo does not belong to the group Birds of Prey.