Domain-Specific Named Entity Recognition via Vector Embeddings
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Solution Overview
Problem
Existing electronic document preparation systems, such as tax return preparation systems, face challenges in accurately understanding and incorporating new tax forms due to inconsistencies in tax-related terms, leading to errors and increased human and computing resources required for integration, which can result in user frustration and legal issues.
Innovation Solution
A method and system that utilize a combination of rules-based analysis, vector-based analysis, and machine learning to identify and match terms from text to known terms, embedding unmatched terms into a vector space and using a machine learning model to determine the best candidate terms, thereby accurately understanding and incorporating tax-related text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional rule-based systems are used to understand tax forms, then implementation is straightforward, but the systems cannot accurately handle term variations and inconsistencies in tax documents
Solution Approach 1:
The patent replaces traditional rule-based mechanical systems with machine learning models that use vector embeddings to understand tax terms. Instead of relying on predefined rules and exact string matching, the system uses neural networks to learn semantic representations of terms, enabling accurate handling of term variations and inconsistencies automatically.
Solution Approach 2:
The patent transforms discrete tax terms into continuous vector representations in a multi-dimensional space. By changing the parameter representation from categorical strings to continuous vectors, the system can capture semantic relationships and handle variations smoothly through geometric operations in the vector space.
2Productivity
If computer systems automatically read and understand tax forms, then human resources are reduced, but term inconsistencies cause significant errors
Solution Approach 1:
The patent replaces brittle rule-based automatic reading systems with robust machine learning models. The neural network-based approach automatically adapts to term variations and inconsistencies in tax documents, maintaining high accuracy while preserving automation efficiency. The system learns from training data to handle real-world document variations without manual rule updates.
3Reliability
If more human resources are allocated to understand and incorporate tax forms, then accuracy improves, but time and cost increase significantly
Solution Approach 1:
The patent enables the system to automatically understand and incorporate tax forms without requiring expert human intervention. The machine learning models self-adjust and learn from the document content, automatically handling term matching and form incorporation. This eliminates the need for tax experts to manually review and validate each term, dramatically reducing integration time while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary training of machine learning models on large corpora of tax documents before deployment. This preliminary action pre-loads the system with knowledge of tax terminology and relationships, enabling it to quickly and accurately process new forms without requiring extensive human review during the actual incorporation process.
4Ease of manufacture
If traditional systems use exact term matching, then implementation is simple, but they fail when terms are used with variations
Solution Approach 1:
The patent transforms exact string matching into semantic similarity comparison in vector space. By representing terms as vectors and using cosine similarity or other distance metrics, the system naturally handles variations, typos, and synonymous expressions while maintaining implementation simplicity through standardized machine learning pipelines.
Data Source
AI summary
Systems and methods for recognizing domain specific named entities are disclosed. An example method may be performed by one or more processors of a text incorporation system and include extracting a number of terms from a text under consideration, identifying, among the number of terms, a set of unmatched terms that do not match any of a plurality of known terms, passing each respective unmatched term to a vectorization module, embedding a vectorized version of each respective unmatched term in a vector space, comparing each vectorized version to known term vectors, passing, to a machine learning model, candidate terms corresponding to known term vectors closest to the vectorized versions, identifying, using the machine learning model, a best candidate term for each respective unmatched term, mapping the best candidate terms to unmatched terms in the text under consideration, and incorporating the text under consideration into the system based on the mappings.