M.A.I. TM (Machine Aided Indexer) is designed to assist humans in the process of creating metadata, indexing documents, and organizing information. MAI utilizes machine learning and natural language processing techniques to automate and streamline the indexing process while maintaining high accuracy and consistency. MAI speeds up the indexing process with a sub-second response rate for indexing records. By combining the strengths of machine learning and human expertise, the result is more accurate, consistent, and efficient metadata creation and document indexing.

There are three elements to the MAI system:

  1. Concept Extractor is the NLP engine which matches controlled vocabulary terms to textual input
  2. The Rule Builder rests on top of the Concept Extractor and provides an interactive NLP interface to easily create commands for the Concept Extractor to increase accuracy of the tagging. Most rules are identity (exact match word-to-word) rules which are automatically created when a term is added. About 20% of the terms in the thesaurus will need a “complex” rule. Some of the complex rule building process is automated but we suggest a human in the loop (HITL) interaction for highest accuracy results.
  3. Statistics Module provides an interactive machine learning capability based on Hit, Miss, and Noise statistics based on actual tagged text to ensure continuous improvement.

Key functionalities and features of MAI:

  • Automated Indexing: Automatically suggests index terms, keywords, or metadata based on the content of the document being indexed. It analyzes the text using more than 20 natural language processing algorithms to identify important concepts and topics.
  • Thesaurus Integration: Integrates with controlled vocabularies such as those managed by Thesaurus Master. This integration ensures that the suggested index terms are consistent with the organization’s terminology standards.
  • Semantic Analysis: Applies semantic analysis techniques to understand the meaning and context of the text, enabling it to generate accurate index terms that reflect the content’s subject matter.
  • User Feedback Mechanism: Includes mechanisms for human indexers to review and validate the suggested index terms. Users can provide feedback on the accuracy and relevance of the suggestions, helping to improve the system’s performance over time.
  • Customization Options: Organizations can customize MAI to align with their specific indexing requirements and domain expertise. This may include fine-tuning the machine learning models, adjusting relevance thresholds, or configuring rules for term suggestion.
  • Workflow Integration: MAI is integrated into existing indexing workflows or content management systems via APIs, allowing seamless collaboration between human indexers and the automated indexing system.
  • Scalability: By automating certain aspects of the indexing process, MAI enables organizations to handle large volumes of content more efficiently, reducing manual effort and increasing productivity.