AI Corpus Enrichment for Knowledge Population
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Solution Overview
Problem
The limited availability of a corpus in a usable form hinders the development of artificial intelligence (AI) based decision support systems, particularly in industries like finance and accounting, where extracting entities and relations from documents is challenging due to varying formats and structures.
Innovation Solution
An AI-based corpus enrichment framework utilizing advanced natural language processing (NLP) and deep learning techniques for annotating, categorizing, and generating a corpus, which includes entity and relation annotation, document categorization, and corpus generation, enabling efficient AI-based decision-making systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional corpus collection methods are used, then the corpus size is limited, but the AI model training effectiveness deteriorates due to insufficient data
Solution Approach 1:
The system performs preliminary actions by automatically collecting documents from multiple sources before AI model training begins. The corpus construction module pre-processes and organizes documents from web crawling, database imports, and user uploads, creating a ready-to-use training corpus that eliminates data scarcity issues during model training.
Solution Approach 2:
The corpus construction module acts as an intermediary between raw document sources and the AI model training process. It mediates by collecting, filtering, and organizing documents from diverse sources (web, databases, user uploads) into a standardized corpus format that is suitable for training, thereby bridging the gap between unstructured data and model requirements.
2Measurement precision
If manual annotation methods are used, then the annotation accuracy is high, but the processing time and cost increase significantly
Solution Approach 1:
The system implements self-service annotation through its AI model that automatically annotates documents. The trained AI model processes new documents autonomously, extracting entities, relationships, and attributes without human intervention. This self-annotation capability maintains high accuracy while dramatically reducing processing time and costs compared to manual methods.
Solution Approach 2:
The system replaces the mechanical manual annotation process with an automated AI-based annotation system. The AI model substitutes human annotators by performing entity recognition, relationship extraction, and attribute identification through computational algorithms, thereby eliminating the time-consuming and labor-intensive nature of manual annotation while maintaining or improving accuracy.
3Adaptability or versatility
If diverse document formats are processed, then the system versatility is improved, but the processing complexity increases
Solution Approach 1:
The corpus construction module is designed with multi-functionality to handle diverse document formats including PDFs, Word documents, Excel sheets, and web pages. It performs multiple functions (parsing, extraction, validation, normalization) within a single unified processing pipeline, enabling the system to process various formats without requiring separate specialized processors for each type, thereby managing complexity while maintaining versatility.
Data Source
AI summary
In some examples, artificial intelligence based corpus enrichment for knowledge population and query response may include generating, based on annotated training documents, an entity and relation annotation model, identifying, based on application of the entity and relation annotation model to a document set that is to be annotated, entities and relations between the entities for each document of the document set to generate an annotated document set, and categorizing each annotated document into a plurality of categories. Artificial intelligence based corpus enrichment may include determining whether an identified category includes a specified number of annotated documents, and if not, additional annotated documents may be generated for the identified category that may represent a corpus. Further, artificial intelligence based corpus enrichment may include training, using the corpus, an artificial intelligence based decision support model, and utilizing the artificial intelligence based decision support model to respond to an inquiry.