Difference between revisions of "Natural Language Classification (NLC)"
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* [[Attention]] Mechanism ...[[Transformer]] ...[[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]] | * [[Attention]] Mechanism ...[[Transformer]] ...[[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]] | ||
* [[Generative AI]] ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]] ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]] | * [[Generative AI]] ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]] ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]] | ||
| − | * [[Development]] | + | * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Loop]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Bayes]] ... [[Network Pattern]] |
| + | * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]] | ||
* [https://www.forbes.com/sites/forbestechcouncil/2021/03/31/why-cognitive-agents-that-teach-themselves-will-change-everything/ Why Cognitive Agents That Teach Themselves Will Change Everything - Forbes] | * [https://www.forbes.com/sites/forbestechcouncil/2021/03/31/why-cognitive-agents-that-teach-themselves-will-change-everything/ Why Cognitive Agents That Teach Themselves Will Change Everything - Forbes] | ||
* [https://www.ibm.com/docs/en/opw/8.1.0?topic=ui-watson-natural-language-classifier IBM Watson Natural Language Classifier] [[IBM]] | * [https://www.ibm.com/docs/en/opw/8.1.0?topic=ui-watson-natural-language-classifier IBM Watson Natural Language Classifier] [[IBM]] | ||
Revision as of 05:53, 5 July 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
- Assistants ... Personal Companions ... Agents ... Negotiation ... LangChain
- Attention Mechanism ...Transformer ...Generative Pre-trained Transformer (GPT) ... GAN ... BERT
- Generative AI ... Conversational AI ... OpenAI's ChatGPT ... Perplexity ... Microsoft's Bing ... You ...Google's Bard ... Baidu's Ernie
- Analytics ... Visualization ... Graphical Tools ... Loop ... Diagrams & Business Analysis ... Bayes ... Network Pattern
- Development ... Notebooks ... AI Pair Programming ... Codeless, Generators, Drag n' Drop ... AIOps/MLOps ... AIaaS/MLaaS
- Why Cognitive Agents That Teach Themselves Will Change Everything - Forbes
- IBM Watson Natural Language Classifier IBM
- A New Way to Classify: Watson Natural Language Classifier Tooling IBM
- Intent Classification in 2023: What it is and How it Works?
- Intent Recognition in NLP
- Intent Classification Datasets & Algorithms for Realistic ... - Dasha
- Intent Classification with Rasa and Spacy
Natural Language Classification (NLC) is a form of Natural Language Processing (NLP) that categorizes problems into intents. Intents are categories used in NLC to classify different types of problems, and intent recognition uses machine learning and NLP to associate text data and expression to a given intent. NLC is useful in managing variations in language and can be used in a variety of applications, such as classifying financial risk, categorizing service queries, and enabling virtual agents to understand customer problems. an "intent" refers to the purpose or goal behind a user's text input. It is a way of categorizing the user's intention or desired outcome in communicating with a system.
For example, if a user sends a message to a customer service chatbot saying "I'm having trouble with my account," the intent behind the message is likely to be something like "Account troubleshooting" or "Technical support". By identifying the intent of the user's message, the chatbot can better understand the user's needs and provide an appropriate response. Intent recognition, also known as intent classification, is the process of identifying the intent behind a user's text input. This typically involves using machine learning and natural language processing techniques to analyze the text and predict the most likely intent category. Once the intent is recognized, the system can then provide a corresponding response or take appropriate action. NLC and intent recognition are used in a wide range of applications, such as customer service chatbots, virtual assistants, and voice-activated devices. By understanding the user's intent, these systems can provide more personalized and effective responses, improving the overall user experience.
