Text Classification Model via Finetuning and Hyperparameter Optimization
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Solution Overview
Problem
Traditional search solutions rely on keyword-based document analysis, which is limited in understanding content at a granular level, making it difficult to determine relevant data from the vast amounts of data generated by enterprises.
Innovation Solution
A text classifier machine learning model is created through language model finetuning and hyperparameter optimization, using unlabeled and labeled documents to customize the model for a user's domain, with features like data augmentation and higher-level feature extraction for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based document analysis is used, then search coverage is broad, but understanding precision at granular level deteriorates
Solution Approach 1:
The patent segments the text classification task into multiple processing stages: initial keyword-based filtering, followed by progressive deep learning model analysis. This segmentation allows the system to handle broad search coverage initially, then apply increasingly sophisticated analysis only to relevant portions, achieving granular understanding without requiring the entire system to be maximally complex.
Solution Approach 2:
The patent applies preliminary action by using simpler keyword-based analysis and shallow processing methods first to identify potentially relevant documents. Only after this preliminary filtering does the system apply more complex deep learning models, thereby achieving precise content understanding while avoiding the unnecessary complexity of applying advanced models to all documents indiscriminately.
2Reliability
If traditional keyword-based search is used, then system simplicity is maintained, but data relevance determination deteriorates
Solution Approach 1:
The patent implements a dynamic system that adapts its complexity based on the specific task and data characteristics. The system can dynamically switch between simple keyword-based search for straightforward queries and complex deep learning model analysis for nuanced classification tasks, thereby achieving high data relevance determination while managing system complexity through adaptive behavior.
Solution Approach 2:
The patent creates a universal text analysis platform that incorporates multiple approaches (keyword-based search, shallow NLP, deep learning models) within a single system. This multi-functional architecture allows the system to handle diverse text classification tasks with varying complexity requirements, achieving reliable data relevance determination across different scenarios without requiring separate specialized systems.
3Measurement precision
If deep learning models are applied to all data, then classification accuracy improves, but computational resource consumption increases
Solution Approach 1:
The patent applies partial action by using deep learning models selectively rather than universally. The system employs sophisticated classification algorithms only on documents that pass through preliminary filtering stages and are identified as requiring detailed analysis. This partial application of complex processing achieves high classification accuracy for relevant documents while avoiding the excessive computational resource consumption that would result from applying the same models to all documents in the corpus.
Data Source
AI summary
Techniques for creating a text classifier machine learning (ML) model are described. According to some embodiments, a language processing service finetunes a language ML model on unlabeled documents of a user, and then trains that finetuned language ML model on labeled documents of the user to be a text classifier that is customized for that user’s domain, e.g., the user’s documents. Additionally, the finetuned language ML model may be trained on labeled documents of the user, for prediction objectives for unlabeled data, before being trained as the text classifier.