Cell Document Annotation for Schema-less Table Search
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Solution Overview
Problem
Information extraction from open-ended, schema-less tables in unstructured documents is challenging due to the lack of understanding of table layouts and relationships between cells, making it difficult to identify and search for information without human input.
Innovation Solution
A method and system that utilize a table extraction engine, header detection engine, and cell extraction engine to generate cell documents, which are then annotated using a cell recognition model for natural language processing, enabling classification of terms and extraction of relationships between them, and storing these annotations in a searchable database for efficient querying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tables are extracted from unstructured documents, then information can be organized and accessed, but the tables remain difficult to search and analyze due to lack of structural understanding
Solution Approach 1:
The patent applies preliminary action by pre-processing table images through OCR to extract text content before the main information extraction process. This preliminary text extraction enables subsequent natural language processing and search operations, resolving the contradiction by preparing data in advance to make it searchable and analyzable.
Solution Approach 2:
The patent uses natural language processing tools and domain-specific lexicons as intermediaries between the extracted table data and the search query. These intermediaries translate and interpret the table content, enabling meaningful search and analysis despite the initial lack of structural understanding.
2Measurement precision
If cell documents are annotated with natural language processing, then search accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by using domain-specific lexicons and targeted natural language processing only for relevant cell documents and terms. Instead of uniformly processing all data, it focuses computational resources on specific areas where domain knowledge is needed, improving search accuracy while reducing overall processing time.
Solution Approach 2:
The patent changes parameters by adjusting the depth and scope of natural language processing based on the specific document and query. It dynamically selects which cell documents require detailed annotation versus those that can use simpler matching, balancing search accuracy with processing efficiency.
Data Source
AI summary
Systems and methods for generating and annotating cell documents include extracting tables from a document using a table extraction engine. Headers are extracted for each of the tables using a header detection engine. Cells are extracted from each of the tables using a cell extraction engine. A cell document is generated for each of the cells which are each correlated to corresponding portions of the headers, each cell document recording the correlation between the cells and the headers. Each cell document is annotated to generate annotated cell documents with a cell recognition model trained to perform natural language processing on the cell documents by classifying each term in each of the cell documents and extracting relationships between the terms of each of the cell documents.