NLP Categorization Framework for Imbalanced Unstructured Data
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Solution Overview
Problem
Conventional machine learning models struggle with classifying unstructured and highly imbalanced data, such as investment strategy descriptions and company filings, lacking the ability to accurately categorize and provide explainable results.
Innovation Solution
A machine learning categorization framework utilizing natural language processing models, including filtering, preprocessing, and various NLP models like TF-IDF, Word2Vec, BERT-finetuned, and explainability modules, to classify unstructured data into predefined categories, with a focus on investment strategies and company data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional machine learning models are used for data classification, then structured data can be classified, but unstructured data cannot be accurately classified
Solution Approach 1:
The patent replaces conventional machine learning models with natural language processing networks that are specifically designed to handle unstructured data. The NLP networks transform unstructured text data into structured representations through embedding layers and attention mechanisms, enabling accurate classification of unstructured data types that conventional models cannot process.
2Measurement precision
If natural language processing networks are used to classify unstructured data, then classification accuracy improves, but model complexity increases
Solution Approach 1:
The NLP network is divided into distinct functional modules: embedding layers for text representation, attention mechanisms for feature weighting, and classification layers for category prediction. This segmentation allows each component to be optimized independently and makes the complex model more manageable and interpretable.
Solution Approach 2:
The model performs preliminary text preprocessing and embedding transformations before classification. By pre-processing unstructured data into structured representations and pre-training embedding layers on large corpora, the model reduces the complexity of the main classification task and improves overall efficiency.
3Productivity
If conventional models classify imbalanced data, then processing speed is maintained, but classification reliability deteriorates
Solution Approach 1:
The training process uses periodic techniques such as learning rate scheduling and batch normalization to handle imbalanced data. The model periodically adjusts its learning parameters and normalizes activations across batches, which helps maintain stable training convergence and reliable classification even with imbalanced data distributions.
Solution Approach 2:
The model dynamically adjusts classification thresholds and loss function weights based on data distribution. By changing decision thresholds and applying class-weighted loss functions, the model compensates for imbalanced data and maintains high reliability across all categories while preserving processing speed.
Data Source
AI summary
The embodiments are directed to a system and method for categorizing unstructured data using a machine learning categorization framework with multiple natural language processing (NLP) models. The multiple NLP models in the machine learning categorization framework may be trained using unstructured data associated with a description of an entity and a multiple categories. Once trained, the NLP models are analyzed to determine one or more NLP models that accurately classify data.