Auto-Generating NLP Cartridges from Document Artifacts
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Solution Overview
Problem
Users lack the expertise and knowledge to build cognitive models for unstructured data, and existing systems do not provide an efficient mechanism for auto-generating relevant artifacts, leading to high development costs and complexity in implementing cognitive solutions.
Innovation Solution
A method and system that utilize a cognitive model generation engine to identify and analyze pre-defined artifacts in documents, generating a natural language processing cartridge based on frequency thresholds and relevance, which can be used as a starting point for users to build their cognitive models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually build cognitive models for unstructured data, then model accuracy and customization can be improved, but development costs and complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically identifying relevant artifacts from documents and pre-configuring them as cognitive model components before user intervention. The artifact identification engine scans documents, extracts entities, relationships, and concepts, and prepares them as reusable artifacts that users can directly incorporate into cognitive models, eliminating the need for users to manually build models from scratch while maintaining customization capability.
Solution Approach 2:
The system creates reusable artifacts by copying and adapting patterns from existing documents and data. Once artifacts are identified and validated, they can be copied and reused across multiple cognitive model projects, reducing development complexity while maintaining model accuracy through proven, tested components.
2Productivity
If existing systems are used for cognitive model development, then some basic functionality is provided, but efficiency and productivity remain low due to lack of automated artifact generation
Solution Approach 1:
The artifact identification engine implements self-service by automatically scanning documents, identifying relevant artifacts, and generating cognitive model components without requiring manual user input for each artifact. The system serves itself by autonomously performing the tedious work of artifact extraction and validation, significantly improving development efficiency while increasing the extent of automation in the cognitive model development process.
Solution Approach 2:
The system replaces manual mechanical processes of artifact identification and model building with automated computational processes. The artifact identification engine uses natural language processing and machine learning algorithms to automatically analyze documents and generate artifacts, substituting the manual mechanical work of experts with automated intelligent systems, thereby大幅提高 productivity and automation level.
3Reliability
If comprehensive artifact identification is performed on all documents, then completeness of cognitive models is improved, but processing time and computational resources increase
Solution Approach 1:
The artifact identification engine applies local quality by focusing computational resources on identifying artifacts that are locally relevant to specific cognitive model projects rather than uniformly processing all documents. The system analyzes document metadata, project requirements, and contextual information to selectively identify and extract only the artifacts that are pertinent to the current modeling task, ensuring model completeness while minimizing unnecessary processing time and resource consumption.
Solution Approach 2:
The system implements partial action by performing artifact identification on a subset of documents that are most relevant to the current cognitive model project rather than exhaustively processing all available documents. The artifact identification engine uses filtering and prioritization strategies to focus on high-value documents, achieving sufficient model completeness through partial processing, thereby reducing overall processing time while maintaining reliability.
Data Source
AI summary
An artifact identification engine identifies artifacts from structured and unstructured data in one or more documents based on pre-defined artifacts, by using cognitive annotations. The identified artifacts are analyzed based at least on received inputs. A cartridge that includes artifacts that are relevant to the structured and unstructured data is generated, based on the analyzing.