Topic Modeling Trie for Low-Context Document Categorization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing artificial intelligence models struggle to accurately categorize documents with little meaningful contextual information, such as those in key-value pairs or tabular formats, due to limited training data and inconsistent formats, leading to degraded performance.
Innovation Solution
A novel trie structure is used for topic modeling, where a dictionary is generated from historical document images to extract base words with the highest frequency, forming N-gram groups, and a trie structure is created for efficient classification of input documents based on similarity scores and keyword frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ML models are used for topic categorization, then they can handle documents with contextual information, but they perform poorly on documents with little or no contextual information (key-value pairs, tabular formats)
Solution Approach 1:
The patent segments the document processing into distinct modules: image processing module (for structural analysis), text extraction module (for content extraction), and classification module (for topic assignment). This segmentation allows each module to specialize in handling specific document characteristics, improving overall reliability across diverse formats without requiring the entire system to be optimized for every possible document type simultaneously.
Solution Approach 2:
The patent introduces an intermediary representation layer consisting of extracted features and keywords that mediates between the raw document data and the classification model. This intermediary layer transforms diverse document formats into a unified feature space, enabling the classification model to handle key-value pairs, tabular data, and contextual text uniformly, thus resolving the adaptability issue.
2Reliability
If large amounts of diverse training data are provided to improve model accuracy, then categorization performance improves, but data collection becomes more difficult and time-consuming
Solution Approach 1:
The patent performs preliminary actions by automatically extracting features, keywords, and structural information from documents during the processing pipeline itself. This preliminary extraction creates a reusable feature database that can be used for training without requiring manual annotation of large datasets, significantly reducing the time needed to prepare training data while maintaining model accuracy.
Solution Approach 2:
The system performs self-service by automatically generating its own training data through the image processing and text extraction modules. The extracted features and keywords from processed documents are automatically used to train the classification model, eliminating the need for separate manual data collection and annotation processes, thus reducing time loss.
3Reliability
If complex image processing and text extraction are performed on all historical documents, then the trie structure becomes more comprehensive, but computational intensity and processing time increase
Solution Approach 1:
The patent applies partial action by extracting and processing only the most relevant features and keywords from historical documents, rather than processing every aspect of every document. The system identifies and extracts only the necessary textual and structural information needed for classification, reducing computational intensity while maintaining sufficient accuracy for reliable categorization.
Solution Approach 2:
The patent extracts only the essential information from historical documents - specifically keywords, phrases, and structural patterns - and uses these extracted features to build the trie structure. By taking out only the necessary information rather than processing complete documents, the system reduces computational intensity while maintaining classification accuracy.
Data Source
AI summary
Method includes obtaining historical document images including text that correspond to different document classes; and generating a dictionary using text of the historical document images. The dictionary includes base words occurring with a greatest frequency in each document class. The base words are extracted from the text of the historical document images and arranged in datasets by a document class, where each dataset includes the base words of a same document class that occur with the greatest frequency within that document class. Trie structure is generated using the base words of the datasets that occur with a greatest frequency in each dataset. The trie structure includes internal nodes including root node and leaf nodes in which keys corresponding to the base words occurring with the greatest frequency in each dataset are respectively stored in predefined order. The trie structure is searchable in the predefined order starting with the root node.