Different classifiers are used in various scenarios to automate the process of document understanding and data extraction. Here’s an overview of when to use each type of classifier and their trainers:
1. Keyword Based Classifier
Use When: You have documents that can be easily classified using specific keywords.
Scenario: Simple document classification where certain keywords or phrases are indicative of the document type.
Example: Classifying invoices, purchase orders, and receipts based on the presence of specific words like “Invoice Number,” “Purchase Order,” etc.
2. Keyword Based Classifier Trainer
Use When: You need to create and train a keyword-based classifier.
Scenario: When setting up a new keyword-based classifier and defining the keywords and phrases that should be used for classification.
Example: Training the system to recognize new document types based on custom keywords.
3. Intelligent Keyword Classifier
Use When: You need more advanced keyword-based classification with the ability to handle more complex documents.
Scenario: When simple keyword-based classification is insufficient, and a more intelligent approach is needed to understand the context.
Example: Classifying legal documents, contracts, or multi-page reports where keywords alone might not be sufficient without context.
4. Intelligent Keyword Classifier Trainer
Use When: You need to train an intelligent keyword classifier.
Scenario: When setting up or refining an intelligent keyword-based classifier, teaching it to better understand context and relationships between words.
Example: Training the system to classify complex legal documents with varying structures and keywords.
5. ML Classifier
Use When: You need to classify documents using machine learning models.
Scenario: When dealing with a large variety of document types and structures that require a more sophisticated approach than keyword-based methods.
Example: Classifying a diverse set of financial documents, such as tax forms, financial statements, and invoices, where patterns and structures vary widely.
6. ML Classifier Trainer
Use When: You need to create and train a machine learning-based classifier.
Scenario: When building a custom machine learning model to classify documents based on historical data and examples.
Example: Training the system with labeled datasets of various document types to improve classification accuracy over time.
7. Generative Classifier
Use When: You need a classifier that can generate new text or content based on the input document.
Scenario: When the task requires generating summaries, abstracts, or other types of content based on the document content.
Example: Creating a system that generates executive summaries from long reports or generates brief descriptions from product specifications.
Summary
Keyword Based Classifier: Simple, keyword-based classification.
Keyword Based Classifier Trainer: Setting up and training keyword-based classifiers.
Intelligent Keyword Classifier Trainer: Training advanced keyword-based classifiers.
ML Classifier: Machine learning-based classification for complex documents.
ML Classifier Trainer: Training custom machine learning models for document classification.
Generative Classifier: Generating new text or content based on input documents.
Each classifier and trainer serves a specific purpose, allowing you to choose the appropriate one based on the complexity and requirements of your document processing tasks.
LLM helped me to write this but it’s validated by me.
