Unstructured Text Parsing via ML Segmentation and Metadata Enrichment
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Solution Overview
Problem
Unstructured text containing contact information is difficult for computerized systems to process automatically, as it lacks inherent structure, making it challenging to extract and utilize contact details for archiving, linking, or form-filling in automated systems.
Innovation Solution
The method involves identifying text segments in unstructured text, assigning metadata labels, calculating feature values, and using a trained machine learning system to classify and enrich the text, transforming it into structured format, utilizing dictionaries, pattern matching systems, and external services for enrichment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unstructured text is processed without structure definition, then processing flexibility is maintained, but automated processing capability is lost
Solution Approach 1:
The unstructured text is divided into discrete text segments or tokens that can be individually processed. Each segment is assigned metadata labels and feature values, allowing the system to handle unstructured data in manageable units while maintaining automated processing through systematic segmentation and classification of each component.
Solution Approach 2:
A machine learning system acts as an intermediary between the unstructured text and the structured output. The ML system receives unstructured text segments, processes them through learned patterns and features, and generates structured contact information, thereby enabling automated processing without requiring predefined structure definitions.
2Measurement precision
If manual processing of unstructured text is used, then accuracy in understanding context is improved, but processing speed and productivity decrease
Solution Approach 1:
The machine learning system is trained in advance on large datasets of unstructured text with known contact information structures. This preliminary training enables the system to learn contextual patterns, metadata associations, and feature relationships, allowing it to accurately process new unstructured text automatically without requiring manual context analysis for each input.
Solution Approach 2:
The system processes unstructured text autonomously by automatically segmenting text, calculating feature values, assigning metadata labels, and generating structured contact information without human intervention. The ML system self-corrects and self-improves through continuous processing, maintaining both accuracy and high productivity.
3Reliability
If complex enrichment processes are applied to text segments, then data quality and completeness are improved, but system complexity increases
Solution Approach 1:
The complex enrichment process is broken down into discrete, manageable steps: text segmentation, feature calculation, metadata label assignment, and structured output generation. Each step processes specific aspects of the text independently, allowing the system to achieve high data quality through systematic enrichment while keeping individual processing components relatively simple and well-defined.
Data Source
AI summary
Systems and methods are provided for computerized, automatic processing of unstructured text to extract bits of contact data to which the extracted text can be linked or attributed. Unstructured text is received and text segments within the text are enriched with metadata labels. A machine learning system is trained on, and used to parse feature values for the text segments and the metadata labels to classify text and generate structured text from the unstructured text.