Logical-Element Word Embeddings for Semi-Structured Document Classification
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Solution Overview
Problem
Existing document classification systems struggle with automatic processing of semi-structured documents, particularly reports of death, due to the inclusion of free descriptions and orthographical variants, leading to increased labor costs and decreased classification performance.
Innovation Solution
A document classification apparatus that constructs and updates word embedded spaces by analyzing logical elements within semi-structured documents, using a first word embedded space and a second word embedded space to improve classification accuracy by handling structural information effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual checking is used to handle free descriptions and orthographical variants in reports of death, then classification accuracy is improved, but labor cost and processing time increase significantly
Solution Approach 1:
The patent introduces an intermediary component (the processing circuit with word embedding construction and update mechanisms) that bridges the gap between unstructured free descriptions and structured classification codes. This intermediary processes the free text content and orthographical variants automatically, eliminating the need for manual checking while maintaining high classification accuracy.
Solution Approach 2:
The patent replaces the mechanical manual checking process with an automated information processing system. The processing circuit uses word embedding techniques to automatically handle free descriptions and orthographical variants, substituting human labor with computational processes that achieve both high accuracy and efficiency.
2Productivity
If conventional word embedding construction is used for semi-structured documents, then processing speed is improved, but classification performance decreases due to inability to handle structural information
Solution Approach 1:
The patent segments the document processing into distinct components: extracting text content from specific logical elements (items), constructing separate word embeddings for each item, and then integrating them. This segmentation allows the system to handle semi-structured documents efficiently while preserving the structural information through item-specific processing.
Solution Approach 2:
The patent applies local quality by constructing word embeddings specific to each logical element (item) within the semi-structured document. Each item receives tailored processing based on its specific characteristics, allowing the system to maintain high classification performance while efficiently handling the semi-structured nature of the documents.
Data Source
AI summary
According to one embodiment, a document classification apparatus includes a processing circuit. The processing circuit is configured to: acquire text content for each of logical elements for semi-structured document data including text data stored for each of the logical elements; select logical elements from the logical elements and generating logical element sets each including the logical elements; analyze text contents for the respective logical element sets and constructing respective word embedded spaces; select a first word embedded space and a second word embedded space including a common word shared with the first word embedded space from the word embedded spaces, and update the first word embedded space based on similarity to the common word in the second word embedded space; and output a classification result of the document data using the first word embedded space and embedding information of a feature quantity of a classification target.